fix first two levels of decision tree?
Depending which tree algorithm you want to use you could manually construct the two first levels of the tree. You can just follow the pseudo code explained for example here for the C4.5 tree. Once you have done this you can remove the two features from the data set and create trees for the remaining part of the tree. If you want to create a rpart object you would be required to take some parts of the source and this may be a bit more demanding. Depending on what tree algorithm you use you will just have a binary split at both levels so you will only need to build 4 separate trees and not 264. Note that you may not have the optimal decision tree since after stepping through the first two levels, the country and product type may still be variables that cause a split. But without seeing the data is impossible to tell. 

Side note, it may be valuable to explain the business that country and product type are not the most sensible variables to have in the top of the decision tree. Sometimes it is better to educate the end users than to force machine learning to do something inaccurate. In my experience end users prefer to have a correct solution than a solution that works because people have a gut feeling that it should be in a certain way.