### 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.
outlet_references[(task.dag_id, task.task_id)].add(d.uri)
                    outlet_datasets[DatasetModel.from_public(d)] = None
        all_datasets = outlet_datasets
        all_datasets.update(input_datasets)
        
        stored_datasets: dict[str, DatasetModel] = {}
        new_datasets: list[DatasetModel] = []
        for dataset in all_datasets:
            stored_dataset = session.scalar(
                select(DatasetModel).where(DatasetModel.uri == dataset.uri).limit(1)
            )
            if stored_dataset:
                
                
                
                stored_dataset.is_orphaned = expression.false()
                stored_datasets[stored_dataset.uri] = stored_dataset
            else:
                new_datasets.append(dataset)
        dataset_manager.create_datasets(dataset_models=new_datasets, session=session)
        stored_datasets.update({dataset.uri: dataset for dataset in new_datasets})
        del new_datasets
        del all_datasets
        
        for dag_id, uri_list in dag_references.items():
            dag_refs_needed = {
                DagScheduleDatasetReference(dataset_id=stored_datasets[uri].id, dag_id=dag_id)
                for uri in uri_list
            }
            dag_refs_stored = set(