PERF `PairwiseDistancesReductions` subsequent work

`PairwiseDistancesReductions` have been introduced as a hierarchy of Cython classes to implement back-ends of some scikit-learn algorithms.

Initial work was listed in #22587.

:bulb: See [this presentation](https://docs.google.com/presentation/d/1RwX_P9lnsb9_YRZ0cA88l3VoEYhiMndQYoKLLF_0Dv0/edit?usp=sharing) for a better understanding of the design of `PairwiseDistancesReductions`.

:bulb: **Before working on improving performance, one must profile the current execution of algorithms to see if there are any substantial benefits.**

Subsequent possible work includes:

 - [ ] #26983 
 - [ ] #25097
 - [ ] Support `"precomputed"` distances
 - Implement back-end for other `Estimators` or `Transformers`, in particular, introduce a specialised back-end for which would remove the costly sequential portion around the current call to `kneighbors` or `radius_neighbors` for (non-complete list):
 	- [ ] computing the MST from the data matrix (`mst_from_data_matrix`) see https://github.com/scikit-learn/scikit-learn/pull/25656#pullrequestreview-1326267314
 	- [ ] computing the MST linkage ([`mst_linkage_core`](https://github.com/scikit-learn/scikit-learn/blob/2ab1d81e167512719b6272d4ddf286bba5d3d232/sklearn/cluster/_hierarchical_fast.pyx#L428-L506))
 	- [x] `LocalOutlierFactor` ([no further optimization possible](https://github.com/scikit-learn/scikit-learn/pull/26316))
 	- [ ] `KNeighbors*.predict*`
 	   - [x] #24076 by @Micky774
 	   - [ ] #28219 by @OmarManzoor
 	   - [ ] Implement multi-output support (WIP [here](https://github.com/Micky774/scikit-learn/pull/3))
 	   - [ ] Support `"distance"` weighting
 	- [ ] `RadiusNeighbors*.predict*`
 	   - [x] #26828
 	   - [ ] Implement Euclidean specialization
 	   - [ ] Implement multi-output support
 	   - [ ] Support `"distance"` weighting
 	- some part of `KMeans`
 - [ ] Force the use of `PairwiseDistancesReductions` for F-contiguous arrays
   - use a scikit-learn configuration entry to accept and convert F-contiguous arrays to C-contiguous arrays
 - [ ] Release the GIL before calling `parallel_on_{X|Y}` instead of releasing it in those methods
    - :bulb: I once have tried and I don't think this is possible.
 - [ ] Integrate `mimalloc` to have proper memory allocation for multi-threaded implementations:
    - :bulb: Changing the default implementation of `malloc(1)` might come with unexpected changes and might break the whole ecosystem. Also maintaining it might be costly.
 - [ ] Tests improvements ideas:
   - https://github.com/scikit-learn/scikit-learn/pull/27281#issuecomment-1704334881
   - parametrize old tests for public API backed by `PairwiseDistancesReductions`?
   - systematically test public API on combinations of sparse and dense datasets?
 - [ ] Extend this design for a new back-end for [`pairwise_kernels`](https://github.com/scikit-learn/scikit-learn/blob/7d1c31892d34e6456c467c3d538ecb0ec4e01c35/sklearn/metrics/pairwise.py#L2116)-based interfaces

COMMENT:
> If we want to pursue discussions for improving some interfaces, I think we better do it in dedicated PRs not to pollute the thread of this issue too much. What do you think?

Yes you are right. Thank you for mentioning.

Labels: Performance, cython, Meta-issue