Exactly 
a 65-dimensional vector seems small enough that it should not need further encoding. It’s basically adding 65 numerical attributes to your dataset, which sounds ok.
I would not even create 2D tensors, but really just append the 65 numerical attributes created from the items in the order to the ones you already had, obtaining a vector of 65 + N elements, where N is the number of numerical attributes you already had.
Following from the simple example above with apples, oranges, and lemons, let’s say you also have the total volume and item counts as the first two numerical attributes. An order with an item count of 2, and a total volume of 200, containing 3 apples and 5 lemons would then be represented by the vector [2, 200, 3, 0, 5] (or, represented as a map of attributes, %{ item_count: 2, total_volume: 200, apples: 3, oranges: 0, lemons: 5 }).
I am not an expert about XGBoost, but I think it’s an ensemble of decision trees like random forests, and therefore should be able to ingest such dataset just fine. Each of those 65 additional attributes (representing the quantity of each specific “normalized item” in the order) would be treated just like the other numerical attributes like total volume or item count.
Same goes for KNN, although I assume that in this case you might benefit from normalizing the attributes before feeding them into the model.
