Use Arrow filtering instead of writing a new arrow file for Dataset.filter
Actually table.filter returns a new table in memory, which can fill users RAM.

Therefore it's not a good solution if we want to keep supporting bigger than RAM datastes
Currently the filter method reads the dataset batch by batch to write a new, filtered, arrow file on disk. Therefore all the reading + writing can take some time.

Using a mask directly on the arrow table doesn't do any read or write operation therefore it's significantly quicker.

I think there are two cases:
- if the dataset doesn't have an indices mapping, then one can simply use the arrow filtering on the main arrow table `dataset._data.filter(...)`
- if the dataset an indices mapping, then the mask should be applied on the indices mapping table `dataset._indices.filter(...)`

The indices mapping is used to map between the idx at `dataset[idx]` in `__getitem__` and the idx in the actual arrow table.

The new filter method should therefore be faster, and allow users to pass either a filtering function (that returns a boolean given an example), or directly a mask.

Feel free to discuss this idea in this thread :)

One additional note: the refactor at #2025 would make all the pickle-related stuff work directly with the arrow filtering, so that we only need to change the Dataset.filter method without having to deal with pickle.

cc @theo-m @gchhablani 

related issues: #1796 #1949 