feat: extract feed_info.txt content

[cka-y]

Summary:
Closes #1775
Creates the architecture to extract file content from GTFS files, plus the first extractor: feed_info.txt. A new gtfs-file-data-extractor function receives one file per invocation and delegates to an extractor resolved by file name, so supporting a new file means adding a FileDataExtractor subclass, registering it in extractors/registry.py, and adding the file name to EXTRACTABLE_FILES in pipeline_tasks.py.
Adds a feedinfo table (one row per dataset, FK to gtfsdataset with cascade delete). Additive only — no API spec or existing column changes.

Expected behavior:
After the daily batch processing (batch-process-dataset), we have a list of files registered for content extraction. For each file we trigger the extraction providing the file url and dataset and feed information. The data is extracted and saved to the database.
Tasks are only enqueued for files whose content changed since the previous dataset, and extraction upserts on the dataset, so reprocessing updates in place instead of duplicating.

Testing tips:
Function can be tested by running the batch processing function or by calling the newly created gtfs-file-data-extractor function directly through HTTP after building this branch on DEV/QA. E.g. of running it in QA:

curl -X POST "https://northamerica-northeast1-mobility-feeds-qa.cloudfunctions.net/gtfs-file-data-extractor" \
-H "Authorization: bearer $(gcloud auth print-identity-token)" \
-H "Content-Type: application/json" \
-d '{
  "stable_id": "mdb-1884",
  "dataset_id": "mdb-1884-202508220038",
  "file_name": "feed_info.txt",
  "file_url": "https://storage.googleapis.com/mobilitydata-datasets-prod/mdb-1884/mdb-1884-202508220038/extracted/feed_info.txt"
}'

Note for deploying: api-deployer creates the function, datasets-batch-deployer creates the task queue. Run api-deployer first, otherwise tasks are enqueued against a function that doesn't exist yet.

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