perf: Vectorize get_chunk_slice for faster sharded writes

[mkitti]

Summary

This PR adds vectorized methods to _ShardIndex and _ShardReader for batch chunk slice lookups, significantly reducing per-chunk function call overhead when writing to shards.

Changes

New Methods

_ShardIndex.get_chunk_slices_vectorized: Batch lookup of chunk slices using NumPy vectorized operations instead of per-chunk Python calls.

_ShardReader.to_dict_vectorized: Build a chunk dictionary using vectorized lookup instead of iterating with individual get() calls.

Modified Code Path

In _encode_partial_single, replaced:

shard_dict = {k: shard_reader.get(k) for k in morton_order_iter(chunks_per_shard)}

With vectorized approach:

morton_coords = _morton_order(chunks_per_shard)
chunk_coords_array = np.array(morton_coords, dtype=np.uint64)
shard_dict = shard_reader.to_dict_vectorized(chunk_coords_array, morton_coords)

Benchmark Results

Single Chunk Write to Large Shard

Writing a single 1x1x1 chunk to a shard with 32³ chunks (using test_sharded_morton_write_single_chunk from PR #3712):

Optimization	Time	Speedup vs Main
Main branch (original)	422ms	-
+ Morton optimization (PR #3708)	261ms	1.6x
+ Vectorized get_chunk_slice	95ms	4.4x

Profile Breakdown

Function	Before	After
get_chunk_slice + _localize_chunk	215ms	3ms
to_dict_vectorized loop	81ms	9ms
Total function calls	299k	37k

Checklist

• Add unit tests and/or doctests in docstrings
• Add docstrings and API docs for any new/modified user-facing classes and functions
• New/modified features documented in docs/user-guide/*.md
• Changes documented as a new file in changes/
• GitHub Actions have all passed
• Test coverage is 100% (Codecov passes)