Adds a new apache-airflow-providers-dq provider,
DbApiHook-based data quality checks.

Airflow already has SQL check operators, and many users rely on them for data
quality today. This provider does not replace that path; it adds a small
DQRule / RuleSet layer for checks that need stable rule identity, persisted
history, and a connection to Airflow assets. That makes quality results easier
to inspect over time, lets downstream asset consumers gate on recent quality,
and also gives LLM-assisted workflows one schema to generate when proposing
checks from table context. Execution still goes through existing DbApiHook
connections.

Ships:

• DQRule and RuleSet models for named data quality rules.
• Built-in SQL checks for common table and column checks, executed through
  common.sql / DbApiHook, plus custom_sql for database-specific or more
  complex checks.
• DQCheckOperator and the @task.dq_check TaskFlow decorator.
• A configurable results backend under [dq] results_path for task, run, and
  rule-level history.
• A read-only API and minimal UI plugin for viewing task/run results and rule
  history.
• Experimental asset helpers, asset_quality() and require_quality(), that
  attach provider-owned quality metadata to assets without changing Airflow
  core.
• Documentation and example Dags covering end-to-end usage with and without
  LLM-generated rules.
• The provider addiitonally ships with dq authoring skills, that LLM's can use to generate rules based on the users need. SKILLS https://github.com/gopidesupavan/airflow/blob/f32940bd261b94238256eaced9150dd51329ce3e/providers/dq/src/airflow/providers/dq/skills/dq-rule-authoring/SKILL.md

This first version is intentionally small. It focuses on a deterministic rule
shape, SQL execution through common.sql, persisted results, and lightweight
visibility in the Airflow UI.

Design decisions:

• Results are stored through an object-storage/local-file backend instead of
  adding new metadata DB tables in the first provider drop. This keeps the
  provider self-contained, avoids Airflow core migrations, and lets deployments
  choose a durable store such as S3/GCS/local files via [dq] results_path.
  The backend stores keyed JSON records for task runs, task instances, and
  per-rule history so the UI can read common views without scanning unrelated
  runs.
• Asset support is implemented by extending assets with provider-owned metadata,
  not by changing Airflow core. Static quality configuration is attached to
  Asset.extra["airflow.dq"]; runtime summaries are attached to asset events
  under extra["airflow.dq.result"]. This lets users try asset quality gating
  now, while leaving room to discuss deeper asset integration later if the
  provider gets traction.
• The first release starts with DbApiHook / SQL execution because Airflow
  already has strong provider coverage through common.sql. File and
  object-store data checks are left for a later iteration.

later iteration:

• File/object-store based checks, where Airflow reads data from S3/GCS/local
  files or other object stores and runs quality rules directly against that
  data. This PR deliberately starts with the DbApiHook path first.
• OpenLineage integration for data quality facets.
• More checks implementations
• Trigger support for DQ checks
• DQProfileOperator

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

• Yes

Generated-by: 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.