feat: add progressive_set_intersection to disjoint_set

[Devanik21]

Checklist

• I have read the CONTRIBUTING.md.
• I have performed a self-review of my own code.
• My code follows the style guidelines of this project.
• I have added tests (doctests) for my changes.
• All new and existing tests passed.
• I have added the algorithm to the correct folder (disjoint_set).

Description

This PR adds progressive_set_intersection() to data_structures/disjoint_set/.

What this algorithm solves

Python's built-in set.intersection(*others) is already highly optimized in C.
However, when intersecting many sets (50–100+) or dealing with highly imbalanced sizes (one small set vs several large sets with millions of elements), we can improve practical performance by:

• Sorting all input sets by size (smallest first)
• Starting with a copy of the smallest set
• Progressively intersecting while pruning early when the result becomes empty

This implements the "smallest-first + progressive pruning" heuristic discussed in #14368.

Why add it?

• Strong educational value — clearly demonstrates an important optimization pattern for multi-set operations.
• Pure Python with zero external dependencies.
• Works with any hashable elements.
• Comprehensive doctests included.
• Handles all edge cases gracefully.

Note: For most everyday use cases, Python’s built-in set.intersection() remains the best choice. This module is mainly for learning and teaching the pruning technique.

Complexity

• Time complexity: Worst case ≈ O(min_size × k), but much faster in practice due to early pruning.
• Space complexity: O(size of the smallest set)

Related Issue

Closes #14368

Example Usage

from data_structures.disjoint_set.progressive_set_intersection import progressive_set_intersection

s1 = {1, 2, 3, 4}
s2 = {2, 3, 5, 6}
s3 = {2, 3, 7}

result = progressive_set_intersection(s1, s2, s3)
print(result)  # Output: {2, 3}

---

Thanks to @Starglen and @dinakars777 for the discussion in #14368.
Happy to add more variants (sorted array intersection or bitmap approach) in a follow-up PR if needed.