API: Set __array_priority__ for MatrixExpr and MatrixExprCons

[Zeroto521]

The subclass of np.ndarray uses __array_priority__ to decide the priority.
If we have a (np.ndarray), b (MatrixExpr), and c (MatrixExprCons).

Without setting __array_priority__, __array_priority__ are 0.0.
The priority is the same. And the operation will be called from left to right.
So we call a >= b, it will be a.__ge__(b). Call a + b, it will be a.__add__(b).

With setting __array_priority__. The operation will call the bigger one.
So we call a >= b, it will be b.__le__(a). Call a + b, it will be b.__radd__(a).

This is useful for a + c. It could skip a.__add__(c). And call c.__radd__(a) directly and raise NotImplementError.

See numpy.class.__array_priority__

[Copilot]

Pull request overview

This PR adds __array_priority__ attributes to the MatrixExpr and MatrixExprCons classes to ensure that when these custom array-like types are used in operations with numpy arrays, their custom __array_ufunc__ methods are called instead of numpy's default behavior.

Changes:

• Set __array_priority__ = 100 for MatrixExpr class
• Set __array_priority__ = 101 for MatrixExprCons class
• Added corresponding entry to CHANGELOG.md

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated no comments.

File	Description
src/pyscipopt/matrix.pxi	Added __array_priority__ class attributes to MatrixExpr (priority 100) and MatrixExprCons (priority 101) to control numpy ufunc dispatch order
CHANGELOG.md	Documented the addition of __array_priority__ for both matrix classes

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