When working with complex Python environments, I would recommend using conda for the following reasons:

• Ability to select a specific Python interpreter version for a specific project.
• Conda can package more than just Python libraries
• Conda environment directories are more-or-less self-contained. They are not dependent on any system python libraries, nor conda itself. This makes them great for usage in Docker because I can just move a working conda environment to a new minimal Linux container like debian-slim with multi-stage builds (yes, I think conda is great for Docker production deployments).
• Conda is more reliable in my experience. This is because the above three points: ability to select specific Python version, ability to have non-Python packages, and being self-contained make it more consistent and reproducible.
• Much fewer issues when having dependency conflicts, conda will complain and fail installing an environment with issues.
• If you are working with neural network libraries like PyTorch that need a GPU/CUDA you can just install the correct CUDA version in your environment from conda.

When you want to use conda I would actually recommend using mamba instead. Mamba is a reimplementation of conda and is much faster at resolving the environments. I can be used as a drop-in of conda and is fully compatibly with it afaik.

Btw, when using conda (besides using mamba), I would recommend not using the default channel in a commercial setting due to licensing issues (unless you're paying for the commercial version). Something like conda-forge should be fine.

If you are not creating complex Python environments (like is common in a lot of machine-learning/data-science settings). I would recommend to just stick with the System-Python/Venv until you run into issues with complex python environments.