### 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)

max_tis = min(max_tis, pool_slots_free)
        starved_pools = {pool_name for pool_name, stats in pools.items() if stats["open"] <= 0}
        
        concurrency_map = self.__get_concurrency_maps(states=EXECUTION_STATES, session=session)
        
        num_starving_tasks_total = 0
        
        starved_dags: set[str] = set()
        starved_tasks: set[tuple[str, str]] = set()
        starved_tasks_task_dagrun_concurrency: set[tuple[str, str, str]] = set()
        pool_num_starving_tasks: dict[str, int] = Counter()
        for loop_count in itertools.count(start=1):
            num_starved_pools = len(starved_pools)
            num_starved_dags = len(starved_dags)
            num_starved_tasks = len(starved_tasks)
            num_starved_tasks_task_dagrun_concurrency = len(starved_tasks_task_dagrun_concurrency)
            
            
            
            query = (
                select(TI)
                .with_hint(TI, "USE INDEX (ti_state)", dialect_name="mysql")
                .join(TI.dag_run)
                .where(DR.run_type != DagRunType.BACKFILL_JOB, DR.state == DagRunState.RUNNING)
                .join(TI.dag_model)
                .where(not_(DM.is_paused))
                .where(TI.state == TaskInstanceState.SCHEDULED)
                .options(selectinload(TI.dag_model))
                .order_by(-TI.priority_weight, DR.execution_date, TI.map_index)
            )