numpy: Overloads between scalars and arrays can produce confusing results

Relates https://github.com/numpy/numpy/issues/10404 - can transplant the main info here if need be

Given how NumPy permits converting arrays of size 1 to scalars (regardless of dimension), this can create confusing interplay between overloads of an array and a scalar numeric value, especially if the array type is meant to be implicitly convertible from int or float.

Reproduction code:

sm.def("overload_scalar", [](py::array_t<float>) { return "Vector"; });
sm.def("overload_scalar", [](int) { return "Int"; });

def test_overload_scalar():
    assert m.overload_scalar(0) == "Int"
    assert m.overload_scalar([0]) == "Vector"
    assert m.overload_scalar(np.array([0.])) == "Int"  # What???

As a workaround, a user could define some wrapper type, like scalar_only<T>, and an accompanying type_converter<scalar_only<T>>, which explicitly rejects any containers that are iterable.

Regarding how to solve this, I unfortunately do not have any good ideas (though it would be nice if/when https://github.com/numpy/numpy/pull/10615 lands).

No source file or test is named. Start with the C++/Python reproduction in the issue and trace pybind11's overload resolution and array/scalar conversion paths; review NumPy issue #10404 and PR #10615 for constraints. Done means the behavior and conversion policy are settled for the shown integer, list, and NumPy-array cases.