sagemaker.local bug when inputs are binary files

**Describe the bug**
Hello, I think I encountered a bug in sagemaker.local. I'm trying to test a batch transform with images as input, but I get the following error even before I reach the input_fn of my custom inference script

```
│ 345 │ │ for element in self.splitter.split(file):
│ ❱ 346 │ │ │ if _payload_size_within_limit(buffer + element, size):
│ 347 │ │ │ │ buffer += element
│ 348 │ │ │ else:
│ 349 │ │ │ │ tmp = buffer
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
TypeError: can only concatenate str (not "bytes") to str
```
I am not using a splitter (splitter type is None), as it's not necessary on images.

I believe the problem is in line 343 of MultRecordStrategy class https://github.com/aws/sagemaker-python-sdk/blob/ae3cc1c44244d79ed6187fd26889449672c55af3/src/sagemaker/local/data.py#L326-L352

We can see that the `buffer` variable is assumed to be a string, which means it's assumed that the `file` variable would not refer to a binary object, which should be possible.

**To reproduce**
Just run local batch transform with a single image as input. The model doesn't really matter I think, it will fail before any prediction or interaction between data and the model is made.

**Expected behavior**
I would expect the buffer to be sensitive to weather the file is a string like json or csv, or a binary type like png.

**Screenshots or logs**
See above.

**System information**
A description of your system. Please provide:
- **SageMaker Python SDK version**: 2.237.0
- **Framework name (eg. PyTorch) or algorithm (eg. KMeans)**: Pytorch, custom inference and model
- **Framework version**: 2.5.1
- **Python version**: 3.11
- **CPU or GPU**: Both
- **Custom Docker image (Y/N)**: Y, extending the pytorch-inference:2.5.1-gpu-py311-cu124-ubuntu22.04-sagemaker image

**Additional context**
Add any other context about the problem here.