SageMakerClient CustomSession uses default values instead of passed user session

**PySDK Version**
- [ ] PySDK V2 (2.x)
- [x] PySDK V3 (3.x)

**Describe the bug**
SageMakerClient builds the sagemaker client from botocore.session.get_session(), ignoring the provided user session resulting in cross-account CreateTrainingJob failures.

In sagemaker-core 2.16.0, SageMakerClient.__init__ ([sagemaker/core/utils/utils.py, ~L395–406](https://github.com/aws/sagemaker-python-sdk/blob/v3.15.1/sagemaker-core/src/sagemaker/core/utils/utils.py#L380-L408)) creates self.sagemaker_client from a fresh default botocore session (botocore.session.get_session() → custom_session) rather than the session argument, under a # TODO: Remove post-launch custom service-model loader. The sagemaker-runtime/featurestore/metrics clients correctly use session. Because the class is a SingletonMeta singleton, the first (default-credential) client is reused process-wide ignoring passed in user sessions.

Impact: callers that pass an explicit boto3 session (e.g. ModelTrainer(sagemaker_session=…) → TrainingJob.create(session=…)) have the control-plane call issued under the ambient default AWS profile instead. When the execution role is in a different account than the default profile, CreateTrainingJob fails with `ValidationException: RoleArn: Cross-account pass role is not allowed`.

**To reproduce**
If a user has multiple AWS profiles (all for different accounts) set up in ~/.aws/config and the default in ~/.aws/credentials is NOT the same account as the intended profile that is used to create the SageMaker Session then CreateTrainingJob will fail with `ValidationException: RoleArn: Cross-account pass role is not allowed.`

```python
from sagemaker.core.helper.session_helper import Session
from sagemaker.train import ModelTrainer
from sagemaker.train.configs import SourceCode, Compute, InputData
import boto3

sess = Session(boto_session=boto3.Session(profile_name="acct-A", region_name="us-east-2"))
ModelTrainer(
sagemaker_session=sess, # account A
role="arn:aws:iam:::role/", # account A
training_image=".dkr.ecr.us-east-2.amazonaws.com/img:latest",
compute=Compute(instance_type="ml.m5.xlarge", instance_count=1),
source_code=SourceCode(source_dir="src", entry_script="train.py"),
).train(input_data_config=[InputData(channel_name="train", data_source="s3://bucket/train/")])
# -> ValidationException: RoleArn: Cross-account pass role is not allowed
# (only when your *default* AWS profile is a different account than A)
```

**Expected behavior**
A training job to be created and executed in the specified account. This is a migration from v2 to v3 and the v2 implementation of this works without issue creating training jobs in the targeted accounts based on the profile passed to the Session.

**Screenshots or logs**
If applicable, add screenshots or logs to help explain your problem.

**System information**
A description of your system. Please provide:
- **SageMaker Python SDK version**: 3.15.1
- **Framework name (eg. PyTorch) or algorithm (eg. KMeans)**: SKLearn
- **Framework version**: 1.4-2
- **Python version**: py3
- **CPU or GPU**: CPU
- **Custom Docker image (Y/N)**: N

**Additional context**
This issue also seems to persist to the latest version of 3.16 since the same custom_session is also used to initiate the sagemaker_client.

Commencez dans sagemaker-core/src/sagemaker/core/utils/utils.py autour de SageMakerClient.__init__ (approximativement aux lignes 395–406), puis comparez la manière dont les clients sagemaker-runtime, featurestore et metrics utilisent la session fournie. Exécutez la reproduction fournie avec plusieurs profils et vérifiez que CreateTrainingJob utilise le compte et la région spécifiés plutôt que le profil par défaut.

Rédigé par le modèle d'indexation à partir du texte de l'issue.