Names of files partially matched by S3Prefix differ between local and remote job environments

## **Describe the bug**
Input objects configured with `s3_data_type="S3Prefix"`:
* in remote jobs, will name the files they matched using their full basename.
* In local mode, names of matched files will consist only of the suffix that remains after removal of the prefix.

This behaviour is exhibited by both `TrainingInput` and `ProcessingInput` objects using `s3_data_type="S3Prefix"`.

Here's an example to illustrate this issue:
### Case 1 - partial match of filenames
Given the following S3 folder structure:
```
s3://{BUCKET_NAME}
`-- data
`-- train-data.csv
`-- train-ground-truth.csv
```
And an input definition which only partially matches the desired files (i.e. too short to fully match any of the files, but too long to match only the parent directory)
```python
TrainingInput( # could be replaced by ProcessingInput - the behaviour is the same
s3_data_type="S3Prefix",
s3_data=f"s3://{BUCKET_NAME}/data/train" # S3Prefix partially matches the names of relevant files
),
```
**when ran in local mode**, the training inputs folder will contain:
```yaml
# os.walk of /opt/ml/input/data - local mode
[
('/opt/ml/input/data', ['training'], []),
('/opt/ml/input/data/training', [], ['-data.csv', '-ground-truth.csv'])
]
```
the matching part of the prefix: "train" was removed from the filenames

whereas in *remote mode*:
```yaml
# `os.walk` of /opt/ml/input/data - remote mode
[
('/opt/ml/input/data', ['training'], []),
('/opt/ml/input/data/training', [], ['train-data.csv', 'train-ground-truth.csv'])
]
```
filenames remained unchanged.

### Case 2 - partial match of parent directory name
If the `s3_data` URI only manages to partially match a directory name, the behaviour is similar, but applies to directory names instead.
Given an expanded folder structure:
```
s3://{BUCKET_NAME}
`-- data
`-- train-data.csv
`-- train-ground-truth.csv
`-- raw-data.txt
`-- debug
`-- last.log
```

and S3 URI which partially matches a parent directory name (also matching a parent's sibling, on purpose)
```python
s3_data=f"s3://{BUCKET_NAME}/d" # partial match of both the "data" and "digits" folder
```
will result in (local run):
```yaml
os.walk of /opt/ml/input/data
[
('/opt/ml/input/data', ['training'], []),
('/opt/ml/input/data/training', ['ata', 'ebug'], []),
 ('/opt/ml/input/data/training/ata', [], ['train-ground-truth.csv', 'train-data.csv', 'raw-data.txt']),
 ('/opt/ml/input/data/training/ebug', [], ['last.log'])
]

```
note that "data" and "debug" got renamed to "ata" and "ebug", as suffixes of the ".../d" in the provided prefix, but the names of files contained within were not changed.

whereas remotely:
```yaml
os.walk of /opt/ml/input/data
[
('/opt/ml/input/data', ['training'], ['training-manifest']),
('/opt/ml/input/data/training', ['data', 'debug'], []),
('/opt/ml/input/data/training/data', [], ['train-data.csv', 'raw-data.txt', 'train-ground-truth.csv']),
('/opt/ml/input/data/training/debug', [], ['last.log'])
]
```
both "data" and "debug" retain their original names.

## **To reproduce**
1. Create the following directory structure in S3
```
s3://{BUCKET_NAME}
`-- data
`-- train-data.csv
`-- train-ground-truth.csv
`-- raw-data.txt
`-- debug
`-- last.log
```

2. Create a jupyter notebook with the following contents:
```python
import sagemaker
from sagemaker.workflow.pipeline_context import LocalPipelineSession, PipelineSession
from sagemaker.pytorch import PyTorch
from sagemaker.workflow.steps import TrainingStep, TrainingInput
from sagemaker.workflow.pipeline import Pipeline

LOCAL_RUN = True # or "False" to run remotely
BUCKET_NAME = "" # replace with a valid bucket ID
IAM_ROLE = sagemaker.get_execution_role()

pipeline_session = LocalPipelineSession() if LOCAL_RUN else PipelineSession()

pytorch_estimator = PyTorch(
sagemaker_session=pipeline_session,
role=IAM_ROLE,
instance_type="local" if LOCAL_RUN else "ml.c5.xlarge",
instance_count=1,
framework_version="1.9.1",
py_version="py38",
entry_point="./entry_point.py",
code_location=f"s3://{BUCKET_NAME}/data"
)

step = TrainingStep(
name="dummy-training-step",
step_args=pytorch_estimator.fit(
inputs=TrainingInput(
s3_data_type="S3Prefix",
s3_data=f"s3://{BUCKET_NAME}/data/train" # for a partial match on filenames
# s3_data=f"s3://{BUCKET_NAME}/d" # for a partial match on directory names
),
)
)

pipeline = Pipeline(
name="s3prefix-debug",
steps=[step],
sagemaker_session=pipeline_session
)

pipeline.upsert(
role_arn=IAM_ROLE,
description="debug pipeline"
)

execution = pipeline.start()
```

3. Add an `entry_point.sh` and populate with the following code. It's meant only to report the contents of the relevant part of the container's filesystem.
```python
import os
from pprint import pprint
path = '/opt/ml/input/'
print(f"os.walk of {path}")
pprint(list(os.walk(path)))
```
4. Run the notebook twice; the first time with `LOCAL_RUN` set to `True` first and then `False`. Save the log output of each run.
5. Compare the results of os.walk of both runs.

## **Expected behavior**
Local execution mode should maintain the full basename of matched files/dirs - just like the remote mode does.

## **System information**
A description of your system. Please provide:
- **SageMaker Python SDK version**: v2.126
- **Framework name (eg. PyTorch) or algorithm (eg. KMeans)**: PyTorch
- **Framework version**: 1.9.1
- **Python version**: 3.8
- **CPU or GPU**: CPU
- **Custom Docker image (Y/N)**: N