Data Asset creation for randomly spitted Mltables gives error in AzureML SDK v2

### Operating System

Windows

### Version Information
Python 3.10

attrs 23.2.0
azure-ai-ml 1.16.1
azure-common 1.1.28
azure-core 1.30.1
azure-identity 1.16.0
azure-mgmt-core 1.4.0
azure-storage-blob 12.20.0
azure-storage-file-datalake 12.15.0
azure-storage-file-share 12.16.0
azureml-dataprep 5.1.6
azureml-dataprep-native 41.0.0
azureml-dataprep-rslex 2.22.2
cachetools 5.3.3
certifi 2024.6.2
cffi 1.16.0
charset-normalizer 3.3.2
cloudpickle 2.2.1
colorama 0.4.6
cryptography 42.0.8
google-api-core 2.19.0
google-auth 2.29.0
googleapis-common-protos 1.63.1
idna 3.7
isodate 0.6.1
jsonschema 4.22.0
jsonschema-specifications 2023.12.1
marshmallow 3.21.3
mltable 1.6.1
msal 1.28.0
msal-extensions 1.1.0
msrest 0.7.1
numpy 1.26.4
oauthlib 3.2.2
opencensus 0.11.4
opencensus-context 0.1.3
opencensus-ext-azure 1.1.13
opencensus-ext-logging 0.1.1
packaging 24.0
pandas 2.2.2
pip 24.0
portalocker 2.8.2
proto-plus 1.23.0
protobuf 4.25.3
psutil 5.9.8
pyarrow 16.1.0
pyasn1 0.6.0
pyasn1_modules 0.4.0
pycparser 2.22
pydash 8.0.1
PyJWT 2.8.0
python-dateutil 2.9.0.post0
pytz 2024.1
pywin32 306
PyYAML 6.0.1
referencing 0.35.1
requests 2.32.3
requests-oauthlib 2.0.0
rpds-py 0.18.1
rsa 4.9
setuptools 69.5.1
six 1.16.0
strictyaml 1.7.3
tqdm 4.66.4
typing_extensions 4.12.1
tzdata 2024.1
urllib3 2.2.1
wheel 0.43.0

### Steps to reproduce

import time
import mltable
from azure.ai.ml import MLClient
from azure.ai.ml.entities import Data
from azure.ai.ml.constants import AssetTypes
from azure.identity import DefaultAzureCredential

subscription_id = "<>"
resource_group = "<>"
workspace = "<>"

VERSION = time.strftime("%Y.%m.%d.%H%M%S", time.gmtime())

ml_client = MLClient(
DefaultAzureCredential(), subscription_id, resource_group, workspace
)

data_asset = ml_client.data.get("test-sdkv2", version="2024.06.06.105241")
paths = [{ "folder" : data_asset.path }]

data_mltable = mltable.from_delimited_files(paths, header='all_files_same_headers', delimiter=',' , encoding='utf8', empty_as_string=False)
train_table, test_table = data_mltable.random_split(0.8, seed=223)

train_table.save("./train")
test_table.save("./test")

train_data_asset = Data(
path="./train",
type=AssetTypes.MLTABLE,
description="Training Dataset",
name="SDK2TrainingDataset",
version=VERSION,
)

test_data_asset = Data(
path="./test",
type=AssetTypes.MLTABLE,
description="Testing Dataset",
name="SDK2TestingDataset",
version=VERSION,
)

ml_client.data.create_or_update(train_data_asset )
ml_client.data.create_or_update(test_data_asset )

### Expected behavior

Two Data Assets should be created:

1) SDK2TrainingDataset
2) SDK2TestingDataset

### Actual behavior

Traceback (most recent call last):
File "D:\AMLSDK2\run.py", line 77, in
ml_client.data.create_or_update(train_data_asset)
File "C:\Users\sshaharyaar\AppData\Local\miniconda3\envs\conda_azureml_sdkv2\lib\site-packages\azure\ai\ml\_telemetry\activity.py", line 292, in wrapper
return f(*args, **kwargs)
File "C:\Users\sshaharyaar\AppData\Local\miniconda3\envs\conda_azureml_sdkv2\lib\site-packages\azure\ai\ml\operations\_data_operations.py", line 425, in create_or_update
raise ex
File "C:\Users\sshaharyaar\AppData\Local\miniconda3\envs\conda_azureml_sdkv2\lib\site-packages\azure\ai\ml\operations\_data_operations.py", line 363, in create_or_update
referenced_uris = self._validate(data)
File "C:\Users\sshaharyaar\AppData\Local\miniconda3\envs\conda_azureml_sdkv2\lib\site-packages\azure\ai\ml\_telemetry\activity.py", line 292, in wrapper
return f(*args, **kwargs)
File "C:\Users\sshaharyaar\AppData\Local\miniconda3\envs\conda_azureml_sdkv2\lib\site-packages\azure\ai\ml\operations\_data_operations.py", line 559, in _validate
validate_mltable_metadata(
File "C:\Users\sshaharyaar\AppData\Local\miniconda3\envs\conda_azureml_sdkv2\lib\site-packages\azure\ai\ml\_utils\_data_utils.py", line 68, in validate_mltable_metadata
err_path = ".".join(error.path)
TypeError: sequence item 1: expected str instance, int found

### Addition information

I am using the Credit Card Clients dataset which is available in Azure SDK v2 Documentation. Moreover, I found no example in SDK v2 documentation where data is being split in train and test and then uploaded as data assets.

Reproduce the provided Python script with azure-ai-ml 1.16.1 and inspect validate_mltable_metadata in azure/ai/ml/_utils/_data_utils.py, identified in the traceback. Done means the train and test MLTABLE directories can both be submitted as Data assets without the TypeError, with a regression test or a narrowed failure case.

Written by the indexing model from the issue text.