Efficient way to add variables and constraints through Gurobi Python without enumerating through all elements

I am learning to build optimization models through Gurobi python and I am having some issues finding the pythonic way of defining decision variables and constraints:

Assuming I have these sets:

time={morning, afternoon, evening};
interval={early,late};
food={burger, banana, apple, orange};

and my decision variable is binary eat[time,interval,food]. However I only have a defined set of possible options as below and cannot enumerate all elements of my sets:

time      interval  food  number  value
morning   early   banana   2      500
morning   early   apple    3      600
afternoon early   burger   1      800
evening    late   orane    2      400

so my eat variables can only be the following:

eat[morning,early,banana]
eat[morning,early,apple]
eat[afternoon,early,burger]
eat[evening,late,orange]

and I cannot do:

eat = m.addVars(time, interval, food, name = "Eat", vtype=GRB.BINARY)

I can do something like:

eat = {}
for row in input.to_dict('records'):
         key = (row['time'], row['interval'],row['food'])
         eat[key] =  m.addVar(name = "Eat", vtype=GRB.BINARY)

But I still have trouble defining my objective which is multiplying number and value and eat and I am looking for a more consistent, elegant way:

obj = quicksum(number[i,j,k]*value[i, j, k] * eat[i, j, k] for i in time 
for j in interval for k in food)

The above will enumerate all which is wrong and I tried something like this:

obj = quicksum(number[key]*value[key] * eat[key] \
                 for key in eat)

which limits it to only defined combinations in the dictionary but then I am struggling with constraints when I have to separate elements of dictionary like below:

m.addConstrs(quicksum(eat[i,j,k] for k in food)==1 for i in time for j in interval)

or something like

m.addConstrs(quicksum(eat[morning,j,banana] ==1) for j in interval)

Sorry for the long questions. Any help from optimization/python experts would be great.