Conflict with a CUDA-11 PyTorch installation

XGBoost for Python depends on `nvidia-nccl-cu12`, which is for CUDA 12. I have a PyTorch 2.4.0 installation for CUDA 11.8, but when I use distributed mode, PyTorch picks up on the one installed by XGBoost for NCCL and it gives me problems for my environment.

My workaround is to install the CPU-only version of XGBoost. However, I still want to use XGBoost with CUDA support. It'd be nice if I could use it with `nvidia-nccl-cu11` instead. Not sure what the solution could be (maybe optional groups of dependencies for XGBoost, such as a `cu11` one, etc; or a different package). Note this could be a future problem when CUDA 13 comes out as well.

No specific file or test is named. Start by reviewing XGBoost's Python dependency packaging and the CUDA-enabled installation path, then reproduce the conflict with PyTorch 2.4.0 on CUDA 11.8 in distributed mode. Done means CUDA-enabled XGBoost can coexist with the CUDA 11.8 PyTorch installation without NCCL conflicts, with the supported installation choice documented.

Written by the indexing model from the issue text.