### 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.
from airflow.timetables.interval import _DataIntervalTimetable
        warnings.warn(
            "`DAG.previous_schedule()` is deprecated.",
            category=RemovedInAirflow3Warning,
            stacklevel=2,
        )
        if not isinstance(self.timetable, _DataIntervalTimetable):
            return None
        return self.timetable._get_prev(timezone.coerce_datetime(dttm))
    def get_next_data_interval(self, dag_model: DagModel) -> DataInterval | None:
        """Get the data interval of the next scheduled run.
        For compatibility, this method infers the data interval from the DAG's
        schedule if the run does not have an explicit one set, which is possible
        for runs created prior to AIP-39.
        This function is private to Airflow core and should not be depended on as a
        part of the Python API.
        :meta private:
        """
        if self.dag_id != dag_model.dag_id:
            raise ValueError(f"Arguments refer to different DAGs: {self.dag_id} != {dag_model.dag_id}")
        if dag_model.next_dagrun is None:  
            return None
        data_interval = dag_model.next_dagrun_data_interval
        if data_interval is not None:
            return data_interval
        
        
        
        return self.infer_automated_data_interval(dag_model.next_dagrun)
    def get_run_data_interval(self, run: DagRun | DagRunPydantic) -> DataInterval: