### 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)

from airflow.dag_processing.manager import DagFileProcessorAgent
        self.log.info("Starting the scheduler")
        executor_class, _ = ExecutorLoader.import_default_executor_cls()
        
        pickle_dags = self.do_pickle and executor_class.supports_pickling
        self.log.info("Processing each file at most %s times", self.num_times_parse_dags)
        
        
        async_mode = not self.using_sqlite
        processor_timeout_seconds: int = conf.getint("core", "dag_file_processor_timeout")
        processor_timeout = timedelta(seconds=processor_timeout_seconds)
        if not self._standalone_dag_processor:
            self.processor_agent = DagFileProcessorAgent(
                dag_directory=Path(self.subdir),
                max_runs=self.num_times_parse_dags,
                processor_timeout=processor_timeout,
                dag_ids=[],
                pickle_dags=pickle_dags,
                async_mode=async_mode,
            )
        try:
            self.job.executor.job_id = self.job.id
            if self.processor_agent:
                self.log.debug("Using PipeCallbackSink as callback sink.")
                self.job.executor.callback_sink = PipeCallbackSink(
                    get_sink_pipe=self.processor_agent.get_callbacks_pipe
                )
            else:
                from airflow.callbacks.database_callback_sink import DatabaseCallbackSink