### 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.
dag_references = collections.defaultdict(set)
        outlet_references = collections.defaultdict(set)
        
        outlet_datasets: dict[DatasetModel, None] = {}
        input_datasets: dict[DatasetModel, None] = {}
        
        
        
        
        for dag in dags:
            curr_orm_dag = existing_dags.get(dag.dag_id)
            if not dag.dataset_triggers:
                if curr_orm_dag and curr_orm_dag.schedule_dataset_references:
                    curr_orm_dag.schedule_dataset_references = []
            for dataset in dag.dataset_triggers:
                dag_references[dag.dag_id].add(dataset.uri)
                input_datasets[DatasetModel.from_public(dataset)] = None
            curr_outlet_references = curr_orm_dag and curr_orm_dag.task_outlet_dataset_references
            for task in dag.tasks:
                dataset_outlets = [x for x in task.outlets or [] if isinstance(x, Dataset)]
                if not dataset_outlets:
                    if curr_outlet_references:
                        this_task_outlet_refs = [
                            x
                            for x in curr_outlet_references
                            if x.dag_id == dag.dag_id and x.task_id == task.task_id
                        ]
                        for ref in this_task_outlet_refs:
                            curr_outlet_references.remove(ref)
                for d in dataset_outlets: