Conditional mathematical optimization in R - r

Question

I have the following data frames:
Required <- data.table( Country=c("AT Iron", "AT Energy", "BE Iron", "BE Energy", "BG Iron", "BG Energy"),Prod1=c(5,10,0,5,0,5),Prod2=c(25,5,10,0,0,5))
Supplied <- data.table( Country=c("AT Iron", "AT Energy", "BE Iron", "BE Energy", "BG Iron", "BG Energy"),Prod1=c(10,5,5,10,5,10),Prod2=c(20,20,20,0,15,10))
> Required
Country Prod1 Prod2
1: AT Iron 5 25
2: AT Energy 10 5
3: BE Iron 0 10
4: BE Energy 5 0
5: BG Iron 0 0
6: BG Energy 5 5
> Supplied
Country Prod1 Prod2
1: AT Iron 10 20
2: AT Energy 5 20
3: BE Iron 5 20
4: BE Energy 10 0
5: BG Iron 5 15
6: BG Energy 10 10
"Required" shows the initial material and energy requirements to manufacture two products, and the materials and energy are supplied by three different countries. For example, product 1 would require, for Energy, 10 units from AT, 5 units from BE and 5 units from BG. "Supplied" shows the actual supply capacity of the countries. Following the example, AT cannot supply 10 units of energy but 5 units, so another country must supply the remaining units. I assume that the country with the most net supply capacity (that is, once discounted the initial requirement) will provide the remaining units. In this case, both BE and BG have 5 units of net supply capacity, so both will provide with equal units, 2.5.
I seek an optimization algorithm that creates a new "Required" table, "RequiredNew", considering supply constrains and the above mentioned assumption. The resulting table should look like:
> RequiredNew
Country Prod1 Prod2
1: AT Iron 5 20
2: AT Energy 10 5
3: BE Iron 0 10
4: BE Energy 7.5 0
5: BG Iron 0 5
6: BG Energy 7.5 5
In the link below I posted a similar question which was solved by user digEmAll, so a similar approach would be suitable. However, I rephrased the question so that it becomes clearer and resembles more to my actual data.
Mathematical optimization in R
I apologise by the multiple posts. Thank you in advance.