### 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)

dagbag = DagBag(read_dags_from_db=True)
            for i, dag_id in enumerate(dag_ids):
                dag = dagbag.get_dag(dag_id)
                dr = dag.create_dagrun(
                    state=State.RUNNING,
                    run_id=f"{DagRunType.MANUAL.value}__{i}",
                    dag_hash=dagbag.dags_hash[dag.dag_id],
                )
                dagruns.append(dr)
                for ti in dr.get_task_instances():
                    ti.set_state(state=State.SCHEDULED)
            mock_agent = mock.MagicMock()
            scheduler_job = Job(
                executor=MockExecutor(do_update=False),
            )
            scheduler_job.heartbeat = mock.MagicMock()
            self.job_runner = SchedulerJobRunner(job=scheduler_job, subdir=PERF_DAGS_FOLDER, num_runs=1)
            self.job_runner.processor_agent = mock_agent
            with assert_queries_count(expected_query_count, margin=15):
                with mock.patch.object(DagRun, "next_dagruns_to_examine") as mock_dagruns:
                    query = MagicMock()
                    query.all.return_value = dagruns
                    mock_dagruns.return_value = query
                    self.job_runner._run_scheduler_loop()
    @pytest.mark.parametrize(
        "expected_query_counts, dag_count, task_count, start_ago, schedule_interval, shape",
        [
            
            ([10, 10, 10, 10], 1, 1, "1d", "None", "no_structure"),
            ([10, 10, 10, 10], 1, 1, "1d", "None", "linear"),