[SequenceFeatureExtractor] Rewrite padding logic from pure python to numpy
# What does this PR do?

Resolves #13539 

Since speech models universally use Numpy `float32` arrays as input features (standard way of representing waveforms), it was decided to rewrite `SequenceFeatureExtractor` from pure python lists (akin to traditional tokenizers) to numpy arrays. It will also help with solving some inconsistent normalization issues (#13538, #13585) due to `float->np.float32` conversions.

The feature extractor itself is still dtype-agnostic (can pad `np.float64` in the future if needed), while the model-specific feature extractors were updated to only work with `np.float32`
Also did you notice a speed-up for larger inputs?<|||||>@patrickvonplaten the benchmarking results are pretty promising:

1. Input lengths from 8000 to 16000 (1 sec max), batch size 64, feature_extractor only: 
    - Python: 52.1 ms ± 2.35 ms
    - Numpy: **32.1 ms** ± 1.13 ms

2. Input lengths from 8000 to 160000 (10 sec max), batch size 64, feature_extractor only: 
    - Python: 276 ms ± 950 µs
    - Numpy: **68.2 ms** ± 491 µs
    <|||||>Great job @anton-l - feel free to merge!