Improve performance of np.savefor small arrays by removing the call to _filter_header
when writing the header, as it is known to be done in Python 3.

We use np.save to save a lot of small arrays (which are 3D point, so are just 3 ints/floats),
and we found out that np.save is about 20 times slower than pickle.dumps.

Profiling shows that most of the time is spent in _filter_header,
which (as I understand it) is not really needed anymore.

The result is about 10x improvement on my machine:

import numpy as np
from numpy.lib.format import write_array
import io
array = np.array([2, 3, 4])

%%timeit
buffer = io.BytesIO()
write_array(buffer, array, allow_pickle=False)

Before:
140 µs ± 10.7 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

After:
14.4 µs ± 497 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

For reference, using pickle.dumps is:
6.27 µs ± 142 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

Edit: This PR also changes reading an array to only call this function for older versions which might have been created by Python 2 before this function was added in 2014 (which are still used by default),
resulting in a nice improvement as well:

Reading a v1 array (same as today):
141 µs ± 2.47 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Reading a v3 array now:
56 µs ± 13.6 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
37.3 µs ± 1.11 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) (after more little improvements)