[FEA]: Design operator specialization for cuda.parallel / C Parallel

### Is this a duplicate?

- [x] I confirmed there appear to be no [duplicate issues](https://github.com/NVIDIA/cccl/issues) for this request and that I agree to the [Code of Conduct](CODE_OF_CONDUCT.md)

### Area

cuda.parallel (Python)

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

Currently, cuda.parallel only provides generic version of parallel reduction:

https://github.com/NVIDIA/cccl/blob/83b10c27884dad6006280f6c9b14234f413da704/python/cuda_parallel/cuda/parallel/experimental/algorithms/reduce.py#L167-L172

On the C++ end, we have specialized code paths for some operators:

https://github.com/NVIDIA/cccl/blob/83b10c27884dad6006280f6c9b14234f413da704/cub/cub/warp/specializations/warp_reduce_shfl.cuh#L610-L625

cuda.parallel doesn't use these optimizations, because generic operators are not classified as, say, `cuda::std::plus`.

### Describe the solution you'd like

cuda.parallel should have a way of recognizing standard operators and mapping them to underlying C++ concepts.

### Describe alternatives you've considered

This problem can be split into the interface and implementation components.
On the interface end, one way to achieve that would be through different overloads https://github.com/NVIDIA/cccl/issues/2542.
But we can also consider introspecting the operator, or recognizing built-in functions like `sum` on Python end.
Regardless, before addressing the interface question, we need a machinery to support it on the implementation end.
This issue can be closed by a prototype of an operator specialization machinery that'd allow cuda.parallel to request standard operators like `cuda::std::plus`, `cuda::minimum`, `cuda::maximum`, etc.

### Additional context

_No response_