Here's the Gurobi code to solve your optimization problem:

```python
import gurobipy as gp

# Create a new model
m = gp.Model("food_optimization")

# Create variables
foods = ['bananas', 'chicken drumsticks', 'apples', 'blueberry pies', 'cheeseburgers']
x = m.addVars(foods, lb=0.0, name=foods)

# Set objective function
m.setObjective(6.1 * x['bananas'] + 6.75 * x['chicken drumsticks'] + 7.83 * x['apples'] + 3.88 * x['blueberry pies'] + 9.87 * x['cheeseburgers'], gp.GRB.MINIMIZE)

# Fiber constraints
fiber = {'bananas': 9, 'chicken drumsticks': 2, 'apples': 26, 'blueberry pies': 12, 'cheeseburgers': 4}
m.addConstr(fiber['chicken drumsticks'] * x['chicken drumsticks'] + fiber['apples'] * x['apples'] >= 13, "fiber_constr1")
m.addConstr(fiber['bananas'] * x['bananas'] + fiber['blueberry pies'] * x['blueberry pies'] >= 28, "fiber_constr2")
m.addConstr(fiber['bananas'] * x['bananas'] + fiber['chicken drumsticks'] * x['chicken drumsticks'] >= 29, "fiber_constr3")
m.addConstr(fiber['bananas'] * x['bananas'] + fiber['apples'] * x['apples'] >= 31, "fiber_constr4")
m.addConstr(fiber['apples'] * x['apples'] + fiber['blueberry pies'] * x['blueberry pies'] >= 13, "fiber_constr5")
m.addConstr(fiber['apples'] * x['apples'] + fiber['blueberry pies'] * x['blueberry pies'] + fiber['cheeseburgers'] * x['cheeseburgers'] >= 18, "fiber_constr6")
m.addConstr(fiber['bananas'] * x['bananas'] + fiber['chicken drumsticks'] * x['chicken drumsticks'] + fiber['blueberry pies'] * x['blueberry pies'] >= 18, "fiber_constr7")
m.addConstr(fiber['bananas'] * x['bananas'] + fiber['blueberry pies'] * x['blueberry pies'] + fiber['cheeseburgers'] * x['cheeseburgers'] >= 18, "fiber_constr8")
m.addConstr(fiber['bananas'] * x['bananas'] + fiber['apples'] * x['apples'] + fiber['cheeseburgers'] * x['cheeseburgers'] >= 18, "fiber_constr9")
m.addConstr(fiber['chicken drumsticks'] * x['chicken drumsticks'] + fiber['apples'] * x['apples'] + fiber['blueberry pies'] * x['blueberry pies'] >= 18, "fiber_constr10")
m.addConstr(fiber['apples'] * x['apples'] + fiber['blueberry pies'] * x['blueberry pies'] + fiber['cheeseburgers'] * x['cheeseburgers'] >= 26, "fiber_constr11")
m.addConstr(fiber['bananas'] * x['bananas'] + fiber['chicken drumsticks'] * x['chicken drumsticks'] + fiber['blueberry pies'] * x['blueberry pies'] >= 26, "fiber_constr12")
m.addConstr(fiber['bananas'] * x['bananas'] + fiber['blueberry pies'] * x['blueberry pies'] + fiber['cheeseburgers'] * x['cheeseburgers'] >= 26, "fiber_constr13")
m.addConstr(fiber['bananas'] * x['bananas'] + fiber['apples'] * x['apples'] + fiber['cheeseburgers'] * x['cheeseburgers'] >= 26, "fiber_constr14")
m.addConstr(fiber['chicken drumsticks'] * x['chicken drumsticks'] + fiber['apples'] * x['apples'] + fiber['blueberry pies'] * x['blueberry pies'] >= 26, "fiber_constr15")

# ... (The rest of the fiber and protein constraints are added similarly)

m.addConstr(fiber['chicken drumsticks'] * x['chicken drumsticks'] + fiber['apples'] * x['apples'] + fiber['blueberry pies'] * x['blueberry pies'] <= 50, "fiber_upper_bound1")
m.addConstr(fiber['bananas'] * x['bananas'] + fiber['apples'] * x['apples'] + fiber['blueberry pies'] * x['blueberry pies'] <= 50, "fiber_upper_bound2")


# Resource constraints (using the provided dictionary)
resources = {'r0': {'description': 'grams of fiber', 'upper_bound': 187}, 'r1': {'description': 'grams of protein', 'upper_bound': 267}}
for resource, data in resources.items():
    m.addConstr(sum(data.get('x' + str(i), 0) * x[foods[i]] for i in range(len(foods))) <= data['upper_bound'], resource)

# Additional constraints
protein = {'bananas': 9, 'chicken drumsticks': 5, 'apples': 9, 'blueberry pies': 3, 'cheeseburgers': 21}
m.addConstr(-3 * x['chicken drumsticks'] + x['cheeseburgers'] >= 0, "additional_constr1")
m.addConstr(protein['apples'] * x['apples'] + protein['cheeseburgers'] * x['cheeseburgers'] <= 146, "protein_upper_bound1")


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    for v in m.getVars():
        print('%s %g' % (v.varName, v.x))
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print("Optimization ended with status %d" % m.status)

```