### 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)

"""
        Find zombie task instances and create a TaskCallbackRequest to be handled by the DAG processor.
        Zombie instances are tasks haven't heartbeated for too long or have a no-longer-running LocalTaskJob.
        """
        from airflow.jobs.job import Job
        self.log.debug("Finding 'running' jobs without a recent heartbeat")
        limit_dttm = timezone.utcnow() - timedelta(seconds=self._zombie_threshold_secs)
        with create_session() as session:
            zombies: list[tuple[TI, str, str]] = (
                session.execute(
                    select(TI, DM.fileloc, DM.processor_subdir)
                    .with_hint(TI, "USE INDEX (ti_state)", dialect_name="mysql")
                    .join(Job, TI.job_id == Job.id)
                    .join(DM, TI.dag_id == DM.dag_id)
                    .where(TI.state == TaskInstanceState.RUNNING)
                    .where(
                        or_(
                            Job.state != JobState.RUNNING,
                            Job.latest_heartbeat < limit_dttm,
                        )
                    )
                    .where(Job.job_type == "LocalTaskJob")
                    .where(TI.queued_by_job_id == self.job.id)
                )
                .unique()
                .all()