load_feature_definitions_from_dataframe() doesn't recognize pandas nullable dtypes (Float64, Int64)

**PySDK Version**
PySDK 3.6.0

**Describe the bug**
load_feature_definitions_from_dataframe() in sagemaker.mlops.feature_store only recognizes numpy dtypes (float64, int64, etc.) but not pandas nullable dtypes (Float64, Int64, string). When a DataFrame uses nullable dtypes (common after calling pd.DataFrame.convert_dtypes()), all numeric columns are incorrectly mapped to StringFeatureDefinition.

**To reproduce**
```
import pandas as pd
from sagemaker.mlops.feature_store import load_feature_definitions_from_dataframe

# Create a DataFrame with numpy dtypes (works correctly)
df_numpy = pd.DataFrame({
"id": [1, 2, 3],
"price": [1.1, 2.2, 3.3],
"name": ["a", "b", "c"],
})
print("numpy dtypes:", {c: str(df_numpy[c].dtype) for c in df_numpy.columns})
# {'id': 'int64', 'price': 'float64', 'name': 'object'}

defs = load_feature_definitions_from_dataframe(df_numpy)
for d in defs:
print(f" {d.feature_name}: {d.feature_type}")

# Now convert to pandas nullable dtypes (common pattern)
df_nullable = df_numpy.convert_dtypes()
print("\nnullable dtypes:", {c: str(df_nullable[c].dtype) for c in df_nullable.columns})
# {'id': 'Int64', 'price': 'Float64', 'name': 'string'}

defs = load_feature_definitions_from_dataframe(df_nullable)
for d in defs:
print(f" {d.feature_name}: {d.feature_type}")
```

Root cause

In sagemaker/mlops/feature_store/feature_utils.py, _INTEGER_TYPES and _FLOAT_TYPES only contain lowercase numpy dtype names:

_INTEGER_TYPES = {'int8', 'int16', 'int32', 'int64', 'int_', 'uint8', 'uint16', 'uint32', 'uint64'}
_FLOAT_TYPES = {'float16', 'float32', 'float64', 'float_'}

Pandas nullable dtypes are capitalized (Int64, Float64, etc.) and are not matched.

Suggested fix

Add nullable dtype names to the type sets:

_INTEGER_TYPES = {'int8', 'int16', 'int32', 'int64', 'int_',
'Int8', 'Int16', 'Int32', 'Int64',
'uint8', 'uint16', 'uint32', 'uint64',
'UInt8', 'UInt16', 'UInt32', 'UInt64'}
_FLOAT_TYPES = {'float16', 'float32', 'float64', 'float_',
'Float16', 'Float32', 'Float64'}

Or use case-insensitive comparison in _generate_feature_definition().

**Expected behavior**
Panda nullable types should get properly converted.

**System information**
A description of your system. Please provide:
- **SageMaker Python SDK version**: 3.6.0

I think this got fixed/addressed before.. but maybe that 2.x code didn't carray over to 3.x
https://github.com/aws/sagemaker-python-sdk/pull/3740/changes

从 sagemaker/mlops/feature_store/feature_utils.py 开始，重点关注 _generate_feature_definition() 以及 _INTEGER_TYPES 和 _FLOAT_TYPES 集合。运行提供的 DataFrame 复现，并验证 nullable 的 Int64 和 Float64 列会被映射为数值特征定义，而不是 StringFeatureDefinition，同时现有的 numpy dtype 行为保持不变。

由索引模型根据 Issue 内容生成。