Investigate our neighbors algorithm choice

Right now, we are relying on the underlying implementation in umap for our neighbors calculation due to (potentially outdated) matintainability/usability concerns of almost every other library. We use PyNNDescent currently AFAICT:

https://github.com/scverse/scanpy/blob/bb4e605fd59263516f0c97d4bff7783f5fcfa541/src/scanpy/neighbors/__init__.py#L658-L665

in absence of a transformer but I think this should be reconsidered.

https://github.com/nmslib/hnswlib seems to be maintained if not amazingly documented
https://github.com/meilisearch/arroy lacks python bindings but looks to be maintained
https://github.com/facebookresearch/faiss seems to be maintained but like the first option lacks documentation it seems
https://github.com/zilliztech/pyglass top performer on https://ann-benchmarks.com/index.html#algorithms

Overall, we need to make a reasonable judgement about the speed-accuracy-maintainability tradeoff and try to figure out if we should make this change for our users.

See https://ann-benchmarks.com/index.html as well

Start with the neighbors implementation in src/scanpy/neighbors/init.py around lines 658-665, then review the linked PyNNDescent usage and candidate libraries. Compare speed, accuracy, documentation, maintenance, and Python integration using the ann-benchmarks reference. Done means a justified recommendation and a clear decision about whether Scanpy should change its neighbors implementation.