### Apache Airflow version

main (development)

### What happened

When dag processor starts with a sub directory to process then the import errors are recorded with that path. So when there is processor for airflow-dag-processor-0 folder in order to remove import errors it lists all files under airflow-dag-processor-0 folder and deletes those not present. This becomes an issue when there is airflow-dag-processor-1 that records import errors whose files won't be part of airflow-dag-processor-0 folder. 

### What you think should happen instead

The fix would be to have processor_subdir stored in ImportError table so that during querying we only look at import errors relevant to the dag processor and don't delete other items. A fix similar to https://github.com/apache/airflow/pull/33357 needs to be applied for import errors as well.

### How to reproduce

1. create a dag file with import error at `~/airflow/dags/airflow-dag-processor-0/sample_sleep.py` . Start a dag processor with -S to process "~/airflow/dags/airflow-dag-processor-0/" . Import error should be present.
2. create a dag file with import error at `~/airflow/dags/airflow-dag-processor-1/sample_sleep.py` . Start a dag processor with -S to process "~/airflow/dags/airflow-dag-processor-1/". Import error for airflow-dag-processor-0 is deleted.
3. 

```python
from datetime import datetime, timedelta

from airflow import DAG
from airflow.decorators import task

from datetime import timedelta, invalid


with DAG(
    dag_id="task_duration",
    start_date=datetime(2023, 1, 1),
    catchup=True,
    schedule_interval="@daily",
) as dag:

    @task
    def sleeper():
        pass

    sleeper()
```

### Operating System

Ubuntu

### Versions of Apache Airflow Providers

_No response_

### Deployment

Virtualenv installation

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [X] 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)

.outerjoin(dag_run, dr_join_cond)
        .outerjoin(task_instance, ti_join_cond)
        .where(or_(task_instance.c.dag_id.is_(None), dag_run.c.dag_id.is_(None)))
    )
def _move_duplicate_data_to_new_table(
    session, source_table: Table, subquery: Query, uniqueness: list[str], target_table_name: str
):
    """
    When adding a uniqueness constraint we first should ensure that there are no duplicate rows.
    This function accepts a subquery that should return one record for each row with duplicates (e.g.
    a group by with having count(*) > 1).  We select from ``source_table`` getting all rows matching the
    subquery result and store in ``target_table_name``.  Then to purge the duplicates from the source table,
    we do a DELETE FROM with a join to the target table (which now contains the dupes).
    :param session: sqlalchemy session for metadata db
    :param source_table: table to purge dupes from
    :param subquery: the subquery that returns the duplicate rows
    :param uniqueness: the string list of columns used to define the uniqueness for the table. used in
        building the DELETE FROM join condition.
    :param target_table_name: name of the table in which to park the duplicate rows
    """
    bind = session.get_bind()
    dialect_name = bind.dialect.name
    query = (
        select(*(source_table.c[x.name].label(str(x.name)) for x in source_table.columns))
        .select_from(source_table)
        .join(subquery, and_(*(source_table.c[x] == subquery.c[x] for x in uniqueness)))
    )
    _create_table_as(
        session=session,
        dialect_name=dialect_name,