API: Special case how numpy scalars are coerced to signed integer

This removes one of the larger changes to array-coercion, which
meant that NumPy scalars were always coerced like a 0-D array
would be (i.e. using normal casting). When the assignment is
explicitly an integer, now `scalar.__int__()` will be used instead
(as was the case previously).
Since previously this was handled differently, a *single* scalar
is still converted using casting:

    np.array(np.float64(np.nan), dtype=np.int64)

succeeds, but any other thing fails, such as:

    np.array([np.float64(np.nan)], dtype=np.int64)
    arr1d_int64[()] = np.float64(np.nan)
    np.array(np.array(np.nan), dtype=np.int64)

This does not affect Python scalars, that always raise, because
they always are converted using `scalar.__int__()`.

Unsigned integers always supported casting from their signed
equivalent, so the difference is much less visible for them and
this chooses to always use the casting behaviour.

The main reason for this change is to help pands:
https://github.com/pandas-dev/pandas/issues/35481

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