…nd configurable search fields (microsoft#5093)

### Motivation and Context

<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
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  1. Why is this change required?
  2. What problem does it solve?
  3. What scenario does it contribute to?
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Today, Azure AI Search connector has two limitations:
1. It's working only with predefined schema, which is not preferred
approach, because index in Azure AI Search is customizable and may use
different schema from the one that exists in SK today.
2. Search operation uses only `Embedding` index field to perform search,
while it's possible to specify multiple vector fields with different
names.

In order to resolve these limitations, major refactoring on abstraction
level is required, which will impact not only Azure AI Search connector,
but all other memory connectors as well, and it will take some time.

This PR contains example that shows how to register Azure AI Search
functionality in Kernel as Plugin and use it to perform search
operations and communicate with AI based on your data. In this example,
index schema is custom and can be changed when needed, as well as
`searchFields` parameter is configurable via `KernelArguments`. The
usage of Azure AI Search plugin is very similar to already existing
approach with `AzureAISearchMemoryStore` connector.

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄

…nd configurable search fields (microsoft#5093)

### Motivation and Context

<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
  1. Why is this change required?
  2. What problem does it solve?
  3. What scenario does it contribute to?
  4. If it fixes an open issue, please link to the issue here.
-->

Today, Azure AI Search connector has two limitations:
1. It's working only with predefined schema, which is not preferred
approach, because index in Azure AI Search is customizable and may use
different schema from the one that exists in SK today.
2. Search operation uses only `Embedding` index field to perform search,
while it's possible to specify multiple vector fields with different
names.

In order to resolve these limitations, major refactoring on abstraction
level is required, which will impact not only Azure AI Search connector,
but all other memory connectors as well, and it will take some time.

This PR contains example that shows how to register Azure AI Search
functionality in Kernel as Plugin and use it to perform search
operations and communicate with AI based on your data. In this example,
index schema is custom and can be changed when needed, as well as
`searchFields` parameter is configurable via `KernelArguments`. The
usage of Azure AI Search plugin is very similar to already existing
approach with `AzureAISearchMemoryStore` connector.

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄