### 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.
"""
        Return (and lock) a list of Dag objects that are due to create a new DagRun.
        This will return a resultset of rows that is row-level-locked with a "SELECT ... FOR UPDATE" query,
        you should ensure that any scheduling decisions are made in a single transaction -- as soon as the
        transaction is committed it will be unlocked.
        """
        from airflow.models.dataset import DagScheduleDatasetReference, DatasetDagRunQueue as DDRQ
        
        dataset_triggered_dag_info = {
            x.dag_id: (x.first_queued_time, x.last_queued_time)
            for x in session.execute(
                select(
                    DagScheduleDatasetReference.dag_id,
                    func.max(DDRQ.created_at).label("last_queued_time"),
                    func.min(DDRQ.created_at).label("first_queued_time"),
                )
                .join(DagScheduleDatasetReference.queue_records, isouter=True)
                .group_by(DagScheduleDatasetReference.dag_id)
                .having(func.count() == func.sum(case((DDRQ.target_dag_id.is_not(None), 1), else_=0)))
            )
        }
        dataset_triggered_dag_ids = set(dataset_triggered_dag_info)
        if dataset_triggered_dag_ids:
            exclusion_list = set(
                session.scalars(
                    select(DagModel.dag_id)
                    .join(DagRun.dag_model)
                    .where(DagRun.state.in_((DagRunState.QUEUED, DagRunState.RUNNING)))