Provide empty flare provider to avoid segfault because of nil provider

[Kaderinho]

What does this PR do?

https://github.com/DataDog/datadog-agent/pull/22010 introduced a Flare provider for logs but this provider is nil when the Logs Agent is disabled. This leads to a segfault because the flare code is not expecting a nil provider

Motivation

Fix a bug caught by e2e tests

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

Reviewer's Checklist

• [ ] If known, an appropriate milestone has been selected; otherwise the Triage milestone is set.
• [ ] Use the major_change label if your change either has a major impact on the code base, is impacting multiple teams or is changing important well-established internals of the Agent. This label will be use during QA to make sure each team pay extra attention to the changed behavior. For any customer facing change use a releasenote.
• [ ] A release note has been added or the changelog/no-changelog label has been applied.
• [ ] Changed code has automated tests for its functionality.
• [ ] Adequate QA/testing plan information is provided. Except if the qa/skip-qa label, with required either qa/done or qa/no-code-change labels, are applied.
• [ ] At least one team/.. label has been applied, indicating the team(s) that should QA this change.
• [ ] If applicable, docs team has been notified or an issue has been opened on the documentation repo.
• [ ] If applicable, the need-change/operator and need-change/helm labels have been applied.
• [ ] If applicable, the k8s/<min-version> label, indicating the lowest Kubernetes version compatible with this feature.
• [ ] If applicable, the config template has been updated.

[pr-commenter[bot]]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: 5acdeec0-ff3d-4f84-b507-9a5b88e27ce2 Baseline: 2f1f3a8ca2b53439343d7fb27a1fed623841f8c8 Comparison: fa3b6bc75c8280b1b08d31b2bca9a1040f484483 Total CPUs: 7

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

No significant changes in experiment optimization goals

Confidence level: 90.00% Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_to_blackhole	% cpu utilization	+0.13	[-6.44, +6.70]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	otel_to_otel_logs	ingress throughput	+0.87	[+0.26, +1.48]
➖	process_agent_real_time_mode	memory utilization	+0.80	[+0.77, +0.83]
➖	file_to_blackhole	% cpu utilization	+0.13	[-6.44, +6.70]
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.00, +0.00]
➖	trace_agent_msgpack	ingress throughput	+0.00	[-0.00, +0.00]
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.00, +0.00]
➖	trace_agent_json	ingress throughput	-0.02	[-0.04, +0.01]
➖	file_tree	memory utilization	-0.04	[-0.12, +0.04]
➖	process_agent_standard_check	memory utilization	-0.11	[-0.14, -0.07]
➖	idle	memory utilization	-0.22	[-0.25, -0.19]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.24	[-1.66, +1.18]
➖	process_agent_standard_check_with_stats	memory utilization	-0.44	[-0.47, -0.41]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.77	[-0.83, -0.70]

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".

[Kaderinho]

Also, to future proof this from happening in the future have we thought changing the flare component to check if the provider is not nil ?

I don't see any issue with that but I know @ogaca-dd has some arguments against it so I'll let him explain his thoughts.

[Kaderinho]

/merge

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