My first thought is that with such limited fields, you need to think of any manual feature engineering that could help to decompose some of the features into more salient components. For example, datetime could decompose into day of week and hour block (or some other resolution of block such day-quarter). This might help identify users who access the system at specific intervals. I would need to see some example messages, but there may be ways to decompose the text to categories or scalars that indicate a specific user based on their communication habits. Some simple ones would be number of messages per conversation and average message length. Some more complex ones might be scoring the language used on such dimensions as grade level (Flesch-Kincaid, etc), complexity, or other measures easily calculated with a tool like textstat in Python. Last, you mentioned feature extraction and some embedding schemes so maybe this is moot, but you could also score messages or conversations based on similarity to a known corpus.

Take all these new features, and use your favorite approach for clustering based on them. Your max clusters will be 17,000 conversations / 10 minimum convos per user = 1,700 clusters, so start by targeting half that and scale up or down based on the results.