Improve dpnp.partition implementation

[antonwolfy]

The PR propose to improve implementation and to use dpnp.sort call when

• input array has number of dimensions > 1
• input array has previously not supported integer dtype
• axis keyword is passed (previously not supported)
• sequence of kth is passed (previously not supported)
  In case of ndim > 1 previously the implementation from legacy backend was used, which is significantly slow (see performance comparation below). It used a copy of input data into the shared USM memory and included computations on the host.

This PR proposes to reuse dpnp.sort for all the above cases.
While in case when the legacy implementation is stable and fast (for 1D input array), it will remain, because it relays on std::nth_element from OneDPL.

The benchmark results were collected on PVC with help of the below code:

import dpnp, numpy as np
from dpnp.tests.helper import generate_random_numpy_array

a = generate_random_numpy_array(10**7, dtype=np.float64, seed_value=117)
ia = dpnp.array(a)
%timeit x = dpnp.partition(ia, 513); x.sycl_queue.wait()

Below tables contains data in case of 1D input array (shape=(10**7,)), where the implementation path was kept the same, plus adding support of missing integer dtypes using fallback on the sort function:

Implementation	int32	uint32	int64	uint64	float32	float64	complex64	complex128
old (legacy backend)	7.46 ms	not supported	9.46 ms	not supported	7.39 ms	8.92 ms	10.9 ms	21.2 ms
new (backend + sort)	7.34 ms	10.8 ms	9.48 ms	12.5 ms	7.37 ms	8.89 ms	11 ms	21.2 ms

The following code was used for 2D input array with shape=(104, 104):

import dpnp, numpy as np
from dpnp.tests.helper import generate_random_numpy_array

a = generate_random_numpy_array((10**4, 10**4), dtype=np.float64, seed_value=117)
ia = dpnp.array(a)
%timeit x = dpnp.partition(ia, 1513); x.sycl_queue.wait()

In that case the new implementation is fully based on the sort call:

Implementation	int32	int64	float32	float64	complex64	complex128
old (legacy backend)	6.4 s	6.89 s	7.36 s	7.66 s	8.61 s	10 s
new (sort)	57.4 ms	64.7 ms	62.2 ms	68 ms	77 ms	151 ms

• Have you provided a meaningful PR description?
• Have you added a test, reproducer or referred to an issue with a reproducer?
• Have you tested your changes locally for CPU and GPU devices?
• Have you made sure that new changes do not introduce compiler warnings?
• Have you checked performance impact of proposed changes?
• Have you added documentation for your changes, if necessary?
• Have you added your changes to the changelog?

[github-actions]

View rendered docs @ https://intelpython.github.io/dpnp/pull/2766/index.html

[github-actions]

Array API standard conformance tests for dpnp=0.20.0dev2=py313h509198e_28 ran successfully.
Passed: 1354
Failed: 3
Skipped: 7

[coveralls]

coverage: 81.074% (-0.04%) from 81.117%
when pulling 5f41788 on resolve-gh-2762
into af6205f on master.