### 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)

PoolNotFound,
    TaskConcurrencyLimitReached,
)
from airflow.executors import executor_constants
from airflow.jobs.base_job import BaseJob
from airflow.models import DAG, DagPickle
from airflow.models.dagrun import DagRun
from airflow.models.taskinstance import TaskInstance, TaskInstanceKey
from airflow.ti_deps.dep_context import DepContext
from airflow.ti_deps.dependencies_deps import BACKFILL_QUEUED_DEPS
from airflow.timetables.base import DagRunInfo
from airflow.utils import helpers, timezone
from airflow.utils.configuration import conf as airflow_conf, tmp_configuration_copy
from airflow.utils.session import provide_session
from airflow.utils.state import DagRunState, State, TaskInstanceState
from airflow.utils.types import DagRunType
if TYPE_CHECKING:
    from airflow.models.mappedoperator import MappedOperator
class BackfillJob(BaseJob):
    """
    A backfill job consists of a dag or subdag for a specific time range. It
    triggers a set of task instance runs, in the right order and lasts for
    as long as it takes for the set of task instance to be completed.
    """
    STATES_COUNT_AS_RUNNING = (State.RUNNING, State.QUEUED)
    __mapper_args__ = {"polymorphic_identity": "BackfillJob"}
    @attr.define
    class _DagRunTaskStatus:
        """
        Internal status of the backfill job. This class is intended to be instantiated