Add Exponential Search algorithm to searches module

[cureprotocols]

PR Checklist:

• [x] I have read CONTRIBUTING.md
• [x] This pull request is all my own work -- I have not plagiarized
• [x] I know that pull requests will not be merged if they fail the automated tests
• [x] This PR only changes one algorithm per file and keeps them modular
• [x] All new Python files are placed inside existing logical directories
• [x] All filenames are lowercase with no spaces or dashes
• [x] All functions and variable names follow Python naming conventions
• [x] All function parameters and return values use Python type hints
• [x] All functions include doctests and pass automated testing
• [x] All new algorithms include at least one usage example or explanation

Note: This PR includes three distinct algorithm contributions, each in their own file with full documentation and formatting.

Author: Michael Alexander Montoya (@cureprotocols)

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1. Exponential Search

• Searches efficiently in sorted arrays where the target is closer to the beginning
• Includes a binary search fallback
• Time complexity: O(log i)
• Located in searches/exponential_search.py

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2. Reservoir Sampling

• Samples k random items from a data stream or large dataset without knowing its size in advance
• Time complexity: O(n)
• Space complexity: O(k)
• Includes doctest and usage block
• Located in searches/reservoir_sampling.py

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3. Union-Find (Disjoint Set with Path Compression)

• Efficient data structure to manage disjoint sets
• Supports path compression and union by rank
• Includes doctests and usage block
• Located in data_structures/disjoint_set/union_find.py

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Each implementation includes:

• Type hints
• Descriptive variable names
• Doctests where applicable
• Follows pre-commit formatting

Author: Michael Alexander Montoya (@cureprotocols)