### Apache Airflow version

2.7.0

### What happened

Relates to investigation of #30327

When you define a DAG with a `schedule=datetime.timedelta(days=1)` and `catchup=False` and you manually trigger a run in the past (logical date in the past, e.g. a few days ago) then upon completion of the run another scheduled run is triggered - irrespective of a previous scheduled run was made just before (not a day ago)

### What you think should happen instead

A manual run should not trigger another run as the scheduling in general should follow the delta time definition of the DAG.

### How to reproduce

Use the following example DAG code and enable scheduling:
```
with DAG(
    dag_id="after_workday_delta_regression",
    start_date=pendulum.datetime(2023, 8, 1, tz="UTC"),
    catchup=False,
    schedule=datetime.timedelta(days=1),
    params={"test": 123}
):
    @task
    def test(ti: TaskInstance=None):
        print(ti.execution_date)
    test()
```

Wait a moment and see that one schedule is run automatically.
Then trigger a manual DAG run and set the execution date/logical date a few days into the past.
After execution you see a third run of the DAG just at the time of completion of the manual trigger as execution date. But the first execution was just a few moments before, not respecting the desired delta of 1 day.

### Operating System

Ubuntu 20.04 / Breeze Dev setup in Py 3.8 Container

### Versions of Apache Airflow Providers

not relevant

### Deployment

Other

### Deployment details

Started and tested with latest main branch and breeze.

### Anything else

I tried to find the root cause but was not able to locate it. Suspect it is rooted in scheduler_job_runner.py.
I'd supply a fix but was not able to fund the root cause. Expert knowledge is needed probably.

### 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)

dag_maker.dag_model.next_dagrun == dr.execution_date
        scheduler_job = Job()
        self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=os.devnull)
        self.job_runner.dagbag = dag_maker.dagbag
        scheduler_job.executor = MockExecutor()
        
        self.job_runner.processor_agent = mock.Mock()
        self.job_runner._schedule_dag_run(dr, session)
        session.flush()
        session.refresh(dr)
        assert dr.state == State.FAILED
        
        assert dag_maker.dag_model.next_dagrun_create_after == dr.execution_date + timedelta(days=1)
        
        assert (
            session.query(DagRun).filter(DagRun.state.in_([DagRunState.RUNNING, DagRunState.QUEUED])).count()
            == 0
        )
    @pytest.mark.parametrize(
        "state, expected_callback_msg", [(State.SUCCESS, "success"), (State.FAILED, "task_failure")]
    )
    def test_dagrun_callbacks_are_called(self, state, expected_callback_msg, dag_maker):
        """
        Test if DagRun is successful, and if Success callbacks is defined, it is sent to DagFileProcessor.
        """
        with dag_maker(
            dag_id="test_dagrun_callbacks_are_called",
            on_success_callback=lambda x: print("success"),
            on_failure_callback=lambda x: print("failed"),
            processor_subdir=TEST_DAG_FOLDER,