### Apache Airflow version

2.5.0

### What happened

Very simple dag:

```python3
from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime, timedelta

with DAG(dag_id="hello_world", schedule=timedelta(days=30 * 365), start_date=datetime(1970, 1, 1)) as dag:
    (
        BashOperator(task_id="hello", bash_command="echo hello")
        >> BashOperator(task_id="world", bash_command="echo world")
    )
```

Run it like `airflow dags test hello_world $(date +%Y-%m-%d)`

End of output:
```
[2022-12-04 21:24:02,993] {dagrun.py:606} INFO - Marking run <DagRun hello_world @ 2022-12-04T00:00:00+00:00: manual__2022-12-04T00:00:00+00:00, state:running, queued_at: None. externally triggered: False> successful
[2022-12-04 21:24:03,003] {dagrun.py:657} INFO - DagRun Finished: dag_id=hello_world, execution_date=2022-12-04T00:00:00+00:00, run_id=manual__2022-12-04T00:00:00+00:00, run_start_date=2022-12-04T00:00:00+00:00, run_end_date=2022-12-05 04:24:02.995279+00:00, run_duration=102242.995279, state=success, external_trigger=False, run_type=manual, data_interval_start=2022-12-04T00:00:00+00:00, data_interval_end=2052-11-26T00:00:00+00:00, dag_hash=None
[2022-12-04 21:24:03,004] {dagrun.py:878} WARNING - Failed to record first_task_scheduling_delay metric:
Traceback (most recent call last):
  File "/home/matt/2022/12/04/venv/lib/python3.9/site-packages/airflow/models/dagrun.py", line 866, in _emit_true_scheduling_delay_stats_for_finished_state
    first_start_date = ordered_tis_by_start_date[0].start_date
IndexError: list index out of range
```

### What you think should happen instead

No warning (or, an explanation of what I can do address whatever it's warning about).

### How to reproduce

_No response_

### Operating System

NixOS 22.11 (gnu/linux)

### Versions of Apache Airflow Providers

n/a

### Deployment

Virtualenv installation

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

self,
        unfinished_tis: Sequence[TI],
        finished_tis: list[TI],
        session: Session,
    ) -> tuple[bool, bool]:
        dep_context = DepContext(
            flag_upstream_failed=True,
            ignore_in_retry_period=True,
            ignore_in_reschedule_period=True,
            finished_tis=finished_tis,
        )
        
        
        return (
            any(ut.are_dependencies_met(dep_context=dep_context, session=session) for ut in unfinished_tis),
            dep_context.have_changed_ti_states,
        )
    def _emit_true_scheduling_delay_stats_for_finished_state(self, finished_tis: list[TI]) -> None:
        """
        This is a helper method to emit the true scheduling delay stats, which is defined as
        the time when the first task in DAG starts minus the expected DAG run datetime.
        This method will be used in the update_state method when the state of the DagRun
        is updated to a completed status (either success or failure). The method will find the first
        started task within the DAG and calculate the expected DagRun start time (based on
        dag.execution_date & dag.timetable), and minus these two values to get the delay.
        The emitted data may contains outlier (e.g. when the first task was cleared, so
        the second task's start_date will be used), but we can get rid of the outliers
        on the stats side through the dashboards tooling built.