Speed up `MatrixExpr.sum(axis=...)` via `quicksum`

[Zeroto521]

Is your feature request related to a problem? Please describe.

np.ndarray.sum(axis=...) will use Expr.__add__ in it inner.
And __add__ will return a new Expr instance each time.
If the matrix is a large dataset, so .sum(axis=...) will become much slower.
quicksum use __iadd__ to avoid creating instance.

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A clear and concise description of what you want to happen.

Inspired by np.apply_along_axis.
We can apply quicksum to .sum(axis=...) too via np.apply_along_axis.

Additional context
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A simple demo to show this feature.
It sppeds up 5.2x than before in the shape of (200, 200).

import time

import numpy as np

from pyscipopt import MatrixExpr, Model, quicksum


def speed_up_sum(self, axis=None):
    """Implementation using np.apply_along_axis"""
    if axis is None:
        return quicksum(self.flat)
    return np.apply_along_axis(quicksum, axis, self).view(MatrixExpr)


if __name__ == "__main__":
    m = Model()
    n = 200
    x = m.addMatrixVar((n, n))

    print(f"Benchmarking sum(axis=1) on {n * n} elements...")
    start = time.time()
    speed_up_sum(x, axis=1)
    end = time.time()
    print(f"np.apply_along_axis: {end - start:.4f} s")
    # np.apply_along_axis: 0.1769 s

    start = time.time()
    x.sum(axis=1)
    end = time.time()
    print(f"np.ndarray.sum: {end - start:.4f} s")
    # np.ndarray.sum: 0.9329 s

[Zeroto521]

#1070

[Joao-Dionisio]

That is a massive speed improvement @Zeroto521 !