Document S3-compatible remote logging in the Amazon provider

[goanpeca]

Add a docs recipe and a system-test example Dag showing how to reuse the existing Amazon provider S3 integration against any S3-compatible object store.

The Amazon provider already talks to S3-compatible endpoints: pointing an aws connection at a custom endpoint_url makes the S3 task handler and ObjectStoragePath("s3://...") write to that endpoint instead of AWS S3. That capability is not documented from the "bring your own S3-compatible bucket" angle, so users rediscover the connection extras (endpoint_url, region_name, path-style addressing) by trial and error. This change writes that path down. Amazon S3 is the baseline, and the recipe notes that the same steps work against other S3-compatible services (for example Amazon S3, Backblaze B2, Cloudflare R2, and MinIO), using placeholder endpoints so the steps are copy-pasteable against any of them.

The change is intentionally small and additive: it adds documentation and one example, and touches no provider or core code.

What is included:

• providers/amazon/docs/logging/s3-compatible-remote-logging.rst: a recipe that maps S3-compatible credentials onto the AWS connection fields, configures [logging] remote logging through that connection, verifies the logs land in the bucket, and reuses the same connection for ObjectStoragePath Dag data. It cross-references the existing write-logs-amazon-s3 handler doc rather than restating it, and it is picked up automatically by the globbed toctree in providers/amazon/docs/logging/index.rst (no manual index edit needed).
• providers/amazon/tests/system/amazon/aws/example_s3_compatible_object_storage.py: an input to transform to output example Dag built on ObjectStoragePath, sitting alongside the other example_s3*.py system tests and wired through the standard get_test_run(dag) harness. Bucket and connection id are read from the environment so the example carries no secrets.

Scope notes for reviewers:

• No new operator, hook, bundle, or provider.yaml entry: this documents existing behavior, so there is nothing new for the registry to pick up.
• The framing is generic S3-compatible object storage. AWS S3 is the baseline; other providers are named only as interchangeable examples with placeholder endpoints.
• No newsfragment: per the contributor docs, providers/ changelogs are regenerated from git log by the release managers and do not consume per-PR newsfragments.

Testing:

• Built the provider docs locally and confirmed the new page renders and appears under the Amazon provider "Logging for Tasks" section, with the cross-reference to the S3 task handler resolving.
• The example Dag is a system test (network access to a real bucket required to execute against a live endpoint) and follows the existing example_s3*.py structure; the module imports and parses cleanly and exposes test_run via get_test_run(dag).
• Ran the repo static checks (ruff / ruff-format) on the new Python file.

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

• Yes (Claude Code, Opus 4.8)

Generated-by: Claude Code (Opus 4.8) following the guidelines

---

• Read the Pull Request Guidelines for more information. Note: commit author/co-author name and email in commits become permanently public when merged.
• For fundamental code changes, an Airflow Improvement Proposal (AIP) is needed.
• When adding dependency, check compliance with the ASF 3rd Party License Policy.
• For significant user-facing changes create newsfragment: {pr_number}.significant.rst, in airflow-core/newsfragments. You can add this file in a follow-up commit after the PR is created so you know the PR number.

[boring-cyborg]

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