Raster/vector join: densify footprint for cross-CRS accuracy

The raster–vector join reprojects the raster footprint (convex hull of the corners, or envelope) with straight chords and no edge densification, so cross-CRS results can under-/over-cover along curved edges. Densifying the footprint edges (~10 points) before reprojection would improve accuracy for large-extent or very-different-CRS pairs.

This tracks the Sedona (Spark) side. The SedonaDB side: https://github.com/apache/sedona-db/issues/1076.

Start by locating the Sedona Spark raster–vector join code that builds and reprojects the raster footprint, then inspect any existing cross-CRS accuracy tests. Verify the behavior on large-extent or very-different-CRS pairs; done means footprint edges are densified to roughly 10 points before reprojection and coverage accuracy improves without regressions.

Written by the indexing model from the issue text.