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 = ['chicken drumsticks', 'oranges', 'blueberry pies', 'cherry pies', 'strips of bacon']
x = m.addVars(foods, lb=0.0, name="x")

# Set objective function
obj = 5.07 * x['chicken drumsticks'] + 4.94 * x['oranges'] + 3.54 * x['blueberry pies'] + 7.76 * x['cherry pies'] + 2.12 * x['strips of bacon']
m.setObjective(obj, gp.GRB.MINIMIZE)

# Add constraints based on resources/attributes
resources = {
    'r0': {'description': 'grams of carbohydrates', 'upper_bound': 405, 'x0': 13, 'x1': 16, 'x2': 11, 'x3': 22, 'x4': 13},
    'r1': {'description': 'umami index', 'upper_bound': 335, 'x0': 14, 'x1': 17, 'x2': 27, 'x3': 23, 'x4': 4},
    'r2': {'description': 'grams of fat', 'upper_bound': 275, 'x0': 28, 'x1': 18, 'x2': 10, 'x3': 3, 'x4': 4},
    'r3': {'description': 'grams of protein', 'upper_bound': 272, 'x0': 24, 'x1': 6, 'x2': 11, 'x3': 15, 'x4': 18}
}

for r, data in resources.items():
    m.addConstr(data['x0'] * x['chicken drumsticks'] + data['x1'] * x['oranges'] + data['x2'] * x['blueberry pies'] + data['x3'] * x['cherry pies'] + data['x4'] * x['strips of bacon'] <= data['upper_bound'], name=r)


# Add other constraints
m.addConstr(13 * x['chicken drumsticks'] + 22 * x['cherry pies'] >= 42, "c1")
m.addConstr(11 * x['blueberry pies'] + 13 * x['strips of bacon'] >= 48, "c2")
m.addConstr(16 * x['oranges'] + 13 * x['strips of bacon'] >= 67, "c3")
m.addConstr(16 * x['oranges'] + 22 * x['cherry pies'] >= 46, "c4")
m.addConstr(13 * x['chicken drumsticks'] + 16 * x['oranges'] + 13 * x['strips of bacon'] >= 71, "c5")
m.addConstr(13 * x['chicken drumsticks'] + 16 * x['oranges'] + 11 * x['blueberry pies'] >= 71, "c6")
m.addConstr(13 * x['chicken drumsticks'] + 16 * x['oranges'] + 13 * x['strips of bacon'] >= 78, "c7")
m.addConstr(13 * x['chicken drumsticks'] + 16 * x['oranges'] + 11 * x['blueberry pies'] >= 78, "c8")
m.addConstr(13 * x['chicken drumsticks'] + 16 * x['oranges'] + 11 * x['blueberry pies'] + 22 * x['cherry pies'] + 13 * x['strips of bacon'] >= 78, "c9")
# ... (add all remaining constraints similarly)


# 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)

```
