Speed up ScalarQuantization by selecting quantiles together

### Description

Currently in `ScalarQuantizer`, `ScalarQuantizer.fromVectorsAutoInterval()` will issue 4 calls (per to scratch-batch, basically `len(vector)/20`) `Selector.select()` and `ScalarQuantizer.fromVectors()` will issue 2 calls. All of these 4/2 calls use the same vectors, just asking for different `k` values. If we use a `multi-select` algorithm, instead of separate `select` algorithms, we can speed up these calls, especially `ScalarQuantizer.fromVectorsAutoInterval()` which is repeating a lot of logic.

The size of the list to select from is practically `20*vector_dimensions`, so this greater speed ups will be observed with larger dimensionality. (Or if `ScalarQuantizer.SCRATCH_SIZE` is ever increased)

Start with ScalarQuantizer.fromVectorsAutoInterval(), ScalarQuantizer.fromVectors(), and Selector.select(); compare how the repeated k selections are assembled. Done means using a multi-select approach for the shared vectors while preserving quantization behavior and validating the intended speedup.

Written by the indexing model from the issue text.