Switch to uv for Python management

[dfsnow]

This PR switches to using uv for environment and dependency management in our data architecture repo and Actions.

Locally, this (optionally) allows users to:

• Use uv for virtual env management (via uv venv && source .venv/bin/activate) with a per-subdirectory virtualenv (i.e. dbt has its own dbt/.venv directory)
• Install the pinned Python version (3.11) into the virtualenv using uv python install
• Install packages into that virtualenv using uv pip install .

For Actions:

• uv now handles caching and installing pip dependencies
• The standard, GitHub-provided setup Action uses the pinned Python version in each project
• We ignore virtual environments, instead using uv to install directly to the system library (just as we did before with plain pip)

Why make this switch?

• uv is significantly faster when installing Python dependencies in Actions (10-100x faster)
• It versions and isolates the dependencies used by each subproject (dbt/, socrata/, etc.) via uv.lock files
• It pins the Python version used by each subproject and uses the same version within Actions via .python-version files

The downsides are:

• It's something new to learn for users. However, the commands are simpler than standard venv and users can still use plain pip and venv if they want to
• It's still slightly hacky to install into the system environment within Actions using uv