[Feature] Add new Intercom fields for real-time performance tracking based on conversation type and rating

### Is there an existing feature request for this?

- [X] I have searched the existing issues

### Describe the Feature

Several metrics are useful for assessing performance more rapidly in Intercom and can be mostly identified via case statements and aggregations.

## Live conversation metrics
Intercom has four states of a conversation: Open, idle, snoozed, closed. Making it easier to track the former three conversation types will allow for better live tracking of Intercom data for end customers.

* Open and snoozed tickets can be derived in `conversations_metrics` model by `conversation_state` based on Intercom API, but aggregates need to be calculated upstream for `total_conversations_open`, `total_conversations_snoozed` in `admin_metrics` and `company_metrics`.
* For idle, `is_idle` boolean can be created in `conversation_metrics` based on lack of `first_admin_response_at` value. Idle conversations can be derived by null values in `first_admin_response_at`. `total_conversations_idle` can then be calculated upstream in `admin_metrics` and `company_metrics`.
* Stretch:`total_conversations_participated` gives us a potential true denominator for each of these metrics--we could examine the conversation parts model. This should be fairly doable for `company_metrics`, but might need some creativity for `admin_metrics` as we'll need to break down which teammates participated in which conversations. We might need to dis-aggregate `all_conversation_admins` to associate conversations with admins.

## Conversation ratings
Intercom has the satisfaction rating to assess CSAT for most customers, looking at the % of conversations that have received a 4 or 5 star rating (a 😀 or 🤩 review from a customer after an Intercom conversation has concluded).

An `is_satisfied` boolean in `conversation_metrics` would be the easiest solve, and then upstream `satisfaction_ratings` to count those totals in `company_metrics` and admin_metrics`.

### Describe alternatives you've considered

Most of these metrics can probably be derived in the current models, but would require a ton of filtering on the dashboards themselves to generate. This provides simpler model metrics from which to perform aggregate calculations.

### Are you interested in contributing this feature?

- [X] Yes.
- [ ] Yes, but I will need assistance and will schedule time during your [office hours](https://calendly.com/fivetran-solutions-team/fivetran-solutions-team-office-hours) for guidance.
- [ ] No.

### Anything else?

_No response_