### Body

Currently `start_date` is mandatory parameter.
For DAGs with `schedule=None` we can relax this requirement as no scheduling calculation needed so the `start_date` parameter isn't used.

### Committer

- [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
session = dag_maker.session
    ds1_id = session.query(DatasetModel.id).filter_by(uri=dataset1.uri).scalar()
    session.bulk_save_objects(
        [
            DatasetDagRunQueue(dataset_id=ds1_id, target_dag_id=dag2.dag_id),
            DatasetDagRunQueue(dataset_id=ds1_id, target_dag_id=dag3.dag_id),
        ]
    )
    session.flush()
    datasets = session.query(DatasetModel.uri).order_by(DatasetModel.id).all()
    info = get_dataset_triggered_next_run_info([dag1.dag_id], session=session)
    assert info[dag1.dag_id] == {
        "ready": 0,
        "total": 1,
        "uri": datasets[0].uri,
    }
    
    info = get_dataset_triggered_next_run_info([dag2.dag_id, dag3.dag_id], session=session)
    assert info[dag2.dag_id] == {
        "ready": 1,
        "total": 2,
        "uri": "",
    }
    assert info[dag3.dag_id] == {
        "ready": 1,
        "total": 3,
        "uri": "",
    }
def test_dag_uses_timetable_for_run_id(session):
    class CustomRunIdTimetable(Timetable):