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[0.46] Finalise support for Numpy 2.0  (#12189)

* Finalise support for Numpy 2.0

This commit brings the Qiskit test suite to a passing state (with all
optionals installed) with Numpy 2.0.0b1, building on previous commits
that handled much of the rest of the changing requirements:

- gh-10890
- gh-10891
- gh-10892
- gh-10897
- gh-11023

Notably, this commit did not actually require a rebuild of Qiskit,
despite us compiling against Numpy; it seems to happen that the C API
stuff we use via `rust-numpy` (which loads the Numpy C extensions
dynamically during module initialisation) hasn't changed.

The main changes are:

- adapting to the changed `copy=None` and `copy=False` semantics in
  `array` and `asarray`.
- making sure all our implementers of `__array__` accept both `dtype`
  and `copy` arguments.

Co-authored-by: Lev S. Bishop <18673315+levbishop@users.noreply.github.com>

* Update `__array__` methods for Numpy 2.0 compatibility

As of Numpy 2.0, implementers of `__array__` are expected and required
to have a signature

    def __array__(self, dtype=None, copy=None): ...

In Numpys before 2.0, the `copy` argument will never be passed, and the
expected signature was

    def __array__(self, dtype=None): ...

Because of this, we have latitude to set `copy` in our implementations
to anything we like if we're running against Numpy 1.x, but we should
default to `copy=None` if we're running against Numpy 2.0.

The semantics of the `copy` argument to `np.array` changed in Numpy 2.0.
Now, `copy=False` means "raise a `ValueError` if a copy is required" and
`copy=None` means "copy only if required".  In Numpy 1.x, `copy=False`
meant "copy only if required".  In _both_ Numpy 1.x and 2.0,
`ndarray.astype` takes a `copy` argument, and in both, `copy=False`
means "copy only if required".  In Numpy 2.0 only, `np.asarray` gained a
`copy` argument with the same semantics as the `np.array` copy argument
from Numpy 2.0.

Further, the semantics of the `__array__` met... (continued)

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