feat(clustering): add SuperClusterAlgorithm for mega-scale marker clustering and configurable badge formatting

[dkhawk]

Summary

This PR introduces SuperClusterAlgorithm, a high-performance hierarchical greedy clustering algorithm designed to handle 100,000 to 1,000,000+ markers on Android with sub-millisecond query performance and minimal garbage collection pressure. It also adds configurable cluster badge precision and compact formatting to both DefaultClusterRenderer and DefaultAdvancedMarkersClusterRenderer, alongside an interactive sample demo.

Key Additions & Features

1. SuperClusterAlgorithm & FlatKdTree:

   • Implements a bottom-up hierarchical zoom pyramid using flat contiguous primitive arrays (DoubleArray, IntArray).
   • Eliminates object allocations (QuadItem, HashSet, HashMap) during runtime pan/zoom, preventing ART GC pauses.
   • Screen-based viewport range queries execute in $O(\log N + K)$ time (< 1 ms across 100k points).
   • Supports incremental location updates, dynamic item additions/removals, and custom cluster radius configuration.

2. Cluster Renderer Formatting Enhancements:

   • Added maxNonZeroDigits: Int (default: 1) for configurable significant digit precision (e.g., 5, 10+, 50+, 100+, 1k+).
   • Added showExactCount: Boolean (default: false) to display exact item counts only when explicitly requested.
   • Added useCompactNumberFormatting: Boolean and compactUnitUppercase: Boolean for SI notation (k/m vs K/M).
   • Implemented reactive clearIconCache() and forceRecluster invalidation on property mutation.

3. SuperCluster100kDemoActivity:

   • Demonstrates 100,000 markers clustered around the San Francisco Bay Area.
   • Custom unclustered marker renderer displaying playful gremlins at high zoom levels.
   • Smooth logarithmic color spectrum across cluster sizes (2 to 100,000+ items).
   • Interactive Material cluster settings dialog allowing real-time adjustment of radius, minClusterSize, non-zero digits, and formatting toggles.

4. Tests & Documentation:

   • Comprehensive unit test suite covering FlatKdTree 2D nearest neighbor queries, zoom pyramid indexing, location updates, invariant proofs, and renderer badge formatting.
   • Performance benchmarks and architectural comparisons in clustering/README.md.