Add Ray community provider for Airflow 3

[jason810496]

Why

Ray is one of the most important AI Infra nowadays.

The existing Ray integration lives outside the Apache Airflow repository (https://github.com/astronomer/astro-provider-ray) and uses Airflow 2 APIs. Bringing it into the community provider ecosystem makes Ray cluster and job orchestration available through supported Airflow 3 interfaces.

How

• Add the apache-airflow-providers-ray distribution with provider metadata, packaging, documentation, and workspace registration.
• Port Ray connection handling, Kubernetes cluster lifecycle operations, job submission operators, deferred triggers, and the @task.ray decorator.
• Migrate Dags and implementation code to public Airflow 3 SDK and provider APIs.
• Add unit coverage for hooks, operators, triggers, decorators, timeout rendering, failure cleanup, and cluster cleanup.
• Add system Dags and KubeRay manifests for existing clusters, dynamically created clusters, and GPU workloads.

Verification

• uv run --project providers/ray --no-sync pytest providers/ray/tests/unit -q (113 passed)
• prek run --files $(git diff --name-only upstream/main...HEAD)
• breeze run mypy $(find providers/ray/src -type f -name '*.py' ! -name '__init__.py' | sort)
• Loaded all providers/ray/tests/system/ray Dags with Airflow 3 DagBag and confirmed no import errors.

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Was generative AI tooling used to co-author this PR?

• Yes, with help of Codex GPT-5 following the guidelines