[java] Fix for OnnxTensor creation when passing in a ByteBuffer containing elements of a different type

[Craigacp]

Description

Fixes a bug where the buffer offset and position was incorrectly computed if the user supplied a ByteBuffer to createTensor but set the type of the tensor to something other than INT8. This would be more common if the user was trying to load the initializers from a serialized representation and didn't want to bother with the type information (which is the case in #21321).

Motivation and Context

Partial fix for #21321. The remainder of the fix is to add a helper which allows users to load initializers out of an onnx_data file, but that will require adding protobuf as a dependency for the Java API to allow the parsing of an ONNX file separately from the native code. It might be nicer to put that functionality into ORT's C API so it can return the lengths & offsets of the initializers when provided with an ONNX file containing external initializers. We hit this kind of thing in Java more often than other languages as in Java models can be supplied as classpath resources which we can easily read, but not materialize on disk for the ORT native library to read.

[leichangqing]

pls merge the code for multiple-model files.

[skottmckay]

/azp run Windows ARM64 QNN CI Pipeline,Windows x64 QNN CI Pipeline,Windows CPU CI Pipeline,Windows GPU CUDA CI Pipeline,Windows GPU DML CI Pipeline,Windows GPU Doc Gen CI Pipeline,Windows GPU TensorRT CI Pipeline,ONNX Runtime Web CI Pipeline,Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline

[skottmckay]

/azp run Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline,Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,orttraining-amd-gpu-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed,onnxruntime-binary-size-checks-ci-pipeline

[skottmckay]

/azp run Big Models,Linux Android Emulator QNN CI Pipeline,Android CI Pipeline,iOS CI Pipeline,ONNX Runtime React Native CI Pipeline

[azure-pipelines]

Azure Pipelines successfully started running 5 pipeline(s).

[azure-pipelines]

Azure Pipelines successfully started running 8 pipeline(s).

[azure-pipelines]

Azure Pipelines successfully started running 10 pipeline(s).