### Apache Airflow version

main (development)

### What happened

Task failing with error "missing upstream values" when running a dag with dynamic task group mapping
error -
```
[2022-11-23, 15:50:07 UTC] {abstractoperator.py:456} ERROR - Cannot expand <Task(_PythonDecoratedOperator): increment_and_verify.hello_there> for run manual__2022-11-23T15:49:59.771915+00:00; missing upstream values: ['x']
```

### What you think should happen instead

It should succeed 

### How to reproduce

Run the below dag code 
```
from airflow import DAG
from airflow.models.taskinstance import TaskInstance
from airflow.operators.dummy import DummyOperator
from datetime import datetime, timedelta
from airflow.decorators import task, task_group
with DAG(
    dag_id="taskmap_taskgroup",
    tags=["AIP_42"],
    start_date=datetime(1970, 1, 1),
    schedule_interval=None,
) as dag:

    @task
    def onetwothree():
        return [1,2,3]

    @task
    def hello_there(arg):
        print(arg)

    @task_group
    def increment_and_verify(x):
        hello_there(x)

    increment_and_verify.expand(x=onetwothree())>>DummyOperator(task_id="done")
```

### Operating System

mac os

### Versions of Apache Airflow Providers

_No response_

### Deployment

Virtualenv installation

### Deployment details

_No response_

### Anything else

_No response_

### 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)

or_(
                                    and_(TR.try_number == try_number, TR.task_id.in_(task_ids))
                                    for try_number, task_ids in task_tries.items()
                                ),
                            )
                            for map_index, task_tries in map_indexes.items()
                        ),
                    )
                    for run_id, map_indexes in run_ids.items()
                ),
            )
            for dag_id, run_ids in task_id_by_key.items()
        )
        delete_qry = TR.__table__.delete().where(conditions)
        session.execute(delete_qry)
    if job_ids:
        from airflow.jobs.base_job import BaseJob
        for job in session.query(BaseJob).filter(BaseJob.id.in_(job_ids)).all():
            job.state = TaskInstanceState.RESTARTING
    if activate_dag_runs is not None:
        warnings.warn(
            "`activate_dag_runs` parameter to clear_task_instances function is deprecated. "
            "Please use `dag_run_state`",
            RemovedInAirflow3Warning,
            stacklevel=2,
        )
        if not activate_dag_runs:
            dag_run_state = False
    if dag_run_state is not False and tis:
        from airflow.models.dagrun import DagRun