### 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.
dags: Collection[DAG],
        processor_subdir: str | None = None,
        session=NEW_SESSION,
    ):
        """
        Ensure the DagModel rows for the given dags are up-to-date in the dag table in the DB.
        Note that this method can be called for both DAGs and SubDAGs. A SubDag is actually a SubDagOperator.
        :param dags: the DAG objects to save to the DB
        :return: None
        """
        if not dags:
            return
        log.info("Sync %s DAGs", len(dags))
        dag_by_ids = {dag.dag_id: dag for dag in dags}
        dag_ids = set(dag_by_ids)
        query = (
            select(DagModel)
            .options(joinedload(DagModel.tags, innerjoin=False))
            .where(DagModel.dag_id.in_(dag_ids))
            .options(joinedload(DagModel.schedule_dataset_references))
            .options(joinedload(DagModel.task_outlet_dataset_references))
        )
        query = with_row_locks(query, of=DagModel, session=session)
        orm_dags: list[DagModel] = session.scalars(query).unique().all()
        existing_dags = {orm_dag.dag_id: orm_dag for orm_dag in orm_dags}
        missing_dag_ids = dag_ids.difference(existing_dags)
        for missing_dag_id in missing_dag_ids:
            orm_dag = DagModel(dag_id=missing_dag_id)
            dag = dag_by_ids[missing_dag_id]
            if dag.is_paused_upon_creation is not None: