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

[dfsnow]

Unlike before, we have to explicitly specify the cache keys for pip dependencies. This is actually probably a good thing insofar as it lets us isolate each of the subproject dependencies in their own cache.

[dfsnow]

This env var is required if we want to install the pip dependencies into the system library rather than a uv virtual env. We could use the uv virtual env but we'd need to switch all the run commands from python $SCRIPT to uv run $SCRIPT and I thought this was easier.

[dfsnow]

Having this file lets us keep our Python version synchronized with uv. If you want to use the Python version associated with the project locally, you just uv python install with the virtualenv active.

[jeancochrane]

Really nice! I tested this out locally and it works great. No concerns from my end.