fix(evaluate): Remove ModelPackageConfig from EvaluateBaseModel steps

When evaluate_base_model=True, the EvaluateBaseModel step in both
DETERMINISTIC_TEMPLATE and CUSTOM_SCORER_TEMPLATE incorrectly included
ModelPackageConfig with SourceModelPackageArn, causing the base model
evaluation to load fine-tuned model weights instead of using only the
base model from the public hub. This made both evaluations identical,
leading users to believe fine-tuning had no effect.

Remove ModelPackageConfig from the EvaluateBaseModel step in both
templates so it only uses BaseModelArn from ServerlessJobConfig. The
EvaluateCustomModel step retains ModelPackageConfig to correctly load
fine-tuned weights. This is consistent with the fix already applied to
the LLMAJ_TEMPLATE.

---
X-AI-Prompt: Fix BenchMarkEvaluator evaluate_base_model bug from D406780217
X-AI-Tool: Kiro

sim: https://t.corp.amazon.com/D406780217

Author: jam-jee

sagemaker-train/src/sagemaker/train/evaluate/pipeline_templates.py

@@ -94,10 +94,6 @@
             "Type": "Training",
             "Arguments": {
                 "RoleArn": "{{ role_arn }}",
-                "ModelPackageConfig": {
-                    "ModelPackageGroupArn": "{{ model_package_group_arn }}",
-                    "SourceModelPackageArn": "{{ source_model_package_arn }}"
-                },
                 "ServerlessJobConfig": {
                     "BaseModelArn": "{{ base_model_arn }}",
                     "AcceptEula": true,
@@ -614,10 +610,6 @@
             "Type": "Training",
             "Arguments": {
                 "RoleArn": "{{ role_arn }}",
-                "ModelPackageConfig": {
-                    "ModelPackageGroupArn": "{{ model_package_group_arn }}",
-                    "SourceModelPackageArn": "{{ source_model_package_arn }}"
-                },
                 "ServerlessJobConfig": {
                     "BaseModelArn": "{{ base_model_arn }}",
                     "AcceptEula": true,