Contributes to #16483 by adding fast paths to `DataFrame.to_cupy` (which is called when `DataFrame.values` is called). The PR follows up #18450 to add cython bindings for `cudf::table_to_array` to pylibcudf and plumbs those changes through cudf classic.

I benchmarked the fast (True) and slow (False) when the dataframe has 1, 6, 20, and 100 columns. The fast paths use `cudf::table_to_array` if the number of columns is greater than 1 and `cp.asarray` directly if the dataframe has only one column. The slow path uses a [raw python loop + assignment](https://github.com/rapidsai/cudf/blob/35d58394e7fb5a090ff3cda351403ec092476af5/python/cudf/cudf/core/frame.py#L520) to create the cupy array.
![image](https://github.com/user-attachments/assets/4c9edfa0-e15d-4902-b597-675cfb02343d)

I used the median because the CUDA overhead of calling `cudf::table_to_array` is large (so there are outliers in the times). Here is a profile of calling `to_cupy` twice for both the slow and fast paths. 
![Screenshot from 2025-05-13 12-23-46](https://github.com/user-attachments/assets/d84fdfa3-3696-4df8-91b6-3eb9dde65430)

In the first calls, the fast path takes 7.3 ms vs 4.8 ms for the slow path. The first call to `cudf::table_to_array` is the bottleneck. But if you compare the second calls, the fast path is much faster (79 us vs 2.3ms)

Authors:
  - Matthew Murray (https://github.com/Matt711)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Matthew Roeschke (https://github.com/mroeschke)

URL: #18801