Numpy 2.x C implementation for AdvancedIncSubtensor1

Description

In our bump to numpy 2.0 we had to stop supporting the C implementation of this Op https://github.com/pymc-devs/pytensor/pull/1194

This was because we were using this PyArrayMapIter functionality that numpy removed. The Op needs to be able to iterate over the first dimension of an array to increment the values provided in y. This increment may need to broadcast with the selected entries.

Example:

import numpy as np
import pytensor
import pytensor.tensor as pt

x = pt.tensor("x", shape=(6, 2, 3))
y = pt.vector("y", shape=(3,))
idxs = pt.vector("idxs", shape=(7,), dtype=int)
out = pt.subtensor.advanced_inc_subtensor1(x, y, idxs)

x_test = np.zeros(x.type.shape)
y_test = np.ones(y.type.shape)
idxs_test = [0, 0, 0, 1, 2, 3, 5]
res = out.eval({x: x_test, y: y_test, idxs: idxs_test})
print(res)
# [[[3. 3. 3.]
#   [3. 3. 3.]]
#  [[1. 1. 1.]
#   [1. 1. 1.]]
#  [[1. 1. 1.]
#   [1. 1. 1.]]
#  [[1. 1. 1.]
#   [1. 1. 1.]]
#  [[0. 0. 0.]
#   [0. 0. 0.]]
#  [[1. 1. 1.]
#   [1. 1. 1.]]]

It's a restricted version of numpy.add.at, where indices must be a single integer vector, whereas numpy.add.at supports arbitrary advanced indexing.

res_np = x_test.copy()
np.add.at(res_np, idxs_test, y_test)
np.testing.assert_allclose(res, res_np)

Start at the pytensor.tensor advanced_inc_subtensor1 entry point and review the NumPy 2.0 limitation described in PR #1194. Use the provided evaluation and np.add.at comparison as acceptance checks; done means the C path works on NumPy 2.x with repeated indices and broadcasting.