### 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)

)
        for pool_name, num_starving_tasks in pool_num_starving_tasks.items():
            Stats.gauge(f"pool.starving_tasks.{pool_name}", num_starving_tasks)
            
            Stats.gauge("pool.starving_tasks", num_starving_tasks, tags={"pool_name": pool_name})
        Stats.gauge("scheduler.tasks.starving", num_starving_tasks_total)
        Stats.gauge("scheduler.tasks.executable", len(executable_tis))
        if executable_tis:
            task_instance_str = "\n".join(f"\t{x!r}" for x in executable_tis)
            self.log.info("Setting the following tasks to queued state:\n%s", task_instance_str)
            
            filter_for_tis = TI.filter_for_tis(executable_tis)
            session.execute(
                update(TI)
                .where(filter_for_tis)
                .values(
                    
                    
                    state=TaskInstanceState.QUEUED,
                    queued_dttm=timezone.utcnow(),
                    queued_by_job_id=self.job.id,
                )
                .execution_options(synchronize_session=False)
            )
            for ti in executable_tis:
                ti.emit_state_change_metric(TaskInstanceState.QUEUED)
        for ti in executable_tis:
            make_transient(ti)
        return executable_tis
    def _enqueue_task_instances_with_queued_state(self, task_instances: list[TI], session: Session) -> None: