Grouping Dataset Events to Trigger DAGs

### Description

_No response_

### Use case/motivation

To handle multiple dataset updates efficiently and avoid triggering a DAG for every small dataset update (like a tiny partition), you can implement a "batching" mechanism where the DAG waits for a group of dataset events before triggering. This way, you avoid redundant DAG runs and ensure the DAG only executes when enough meaningful updates have occurred.

### Related issues

_No response_

### Are you willing to submit a PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

No files, tests, or entry points are named. Start by tracing Airflow's dataset-event scheduling and DAG-triggering paths, then clarify the batching threshold, timing, and event semantics before choosing an implementation. Done means the agreed batching behavior is implemented and covered by tests.

Written by the indexing model from the issue text.