### 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)

@provide_session
    def _emit_pool_metrics(self, session: Session = NEW_SESSION) -> None:
        from airflow.models.pool import Pool
        pools = Pool.slots_stats(session=session)
        for pool_name, slot_stats in pools.items():
            Stats.gauge(f"pool.open_slots.{pool_name}", slot_stats["open"])
            Stats.gauge(f"pool.queued_slots.{pool_name}", slot_stats["queued"])
            Stats.gauge(f"pool.running_slots.{pool_name}", slot_stats["running"])
            Stats.gauge(f"pool.deferred_slots.{pool_name}", slot_stats["deferred"])
            
            Stats.gauge("pool.open_slots", slot_stats["open"], tags={"pool_name": pool_name})
            Stats.gauge("pool.queued_slots", slot_stats["queued"], tags={"pool_name": pool_name})
            Stats.gauge("pool.running_slots", slot_stats["running"], tags={"pool_name": pool_name})
            Stats.gauge("pool.deferred_slots", slot_stats["deferred"], tags={"pool_name": pool_name})
    @provide_session
    def adopt_or_reset_orphaned_tasks(self, session: Session = NEW_SESSION) -> int:
        """
        Adopt or reset any TaskInstance in resettable state if its SchedulerJob is no longer running.
        :return: the number of TIs reset
        """
        self.log.info("Adopting or resetting orphaned tasks for active dag runs")
        timeout = conf.getint("scheduler", "scheduler_health_check_threshold")
        for attempt in run_with_db_retries(logger=self.log):
            with attempt:
                self.log.debug(
                    "Running SchedulerJob.adopt_or_reset_orphaned_tasks with retries. Try %d of %d",
                    attempt.retry_state.attempt_number,
                    MAX_DB_RETRIES,
                )
                self.log.debug("Calling SchedulerJob.adopt_or_reset_orphaned_tasks method")