```json
{
  "sym_variables": [
    ("x0", "cheeseburgers"),
    ("x1", "oreos"),
    ("x2", "bananas")
  ],
  "objective_function": "3*x0 + 3*x1 + 1*x2",
  "constraints": [
    "1*x0 + 2*x1 >= 8",
    "3*x1 + 5*x2 >= 46",
    "8*x0 + 5*x2 >= 35",
    "1*x0 + 7*x2 <= 60",
    "1*x0 + 2*x1 <= 47",
    "1*x0 + 2*x1 + 7*x2 <= 47",
    "3*x1 + 5*x2 <= 113",
    "8*x0 + 5*x2 <= 105",
    "8*x0 + 3*x1 + 5*x2 <= 105",
    "x0 >= 0",
    "x1 >= 0",
    "x2 >= 0",
    "1*x0 <= 68",
    "8*x0 <= 156"


  ]
}
```

```python
import gurobipy as gp

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

# Create variables
cheeseburgers = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="cheeseburgers")
oreos = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="oreos")
bananas = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="bananas")

# Set objective function
model.setObjective(3 * cheeseburgers + 3 * oreos + 1 * bananas, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(1 * cheeseburgers + 2 * oreos >= 8, "protein_constraint1")
model.addConstr(3 * oreos + 5 * bananas >= 46, "tastiness_constraint1")
model.addConstr(8 * cheeseburgers + 5 * bananas >= 35, "tastiness_constraint2")
model.addConstr(1 * cheeseburgers + 7 * bananas <= 60, "protein_constraint2")
model.addConstr(1 * cheeseburgers + 2 * oreos <= 47, "protein_constraint3")
model.addConstr(1 * cheeseburgers + 2 * oreos + 7 * bananas <= 47, "protein_constraint4")
model.addConstr(3 * oreos + 5 * bananas <= 113, "tastiness_constraint3")
model.addConstr(8 * cheeseburgers + 5 * bananas <= 105, "tastiness_constraint4")
model.addConstr(8 * cheeseburgers + 3 * oreos + 5 * bananas <= 105, "tastiness_constraint5")


# Add upper bound constraints based on resources
model.addConstr(1 * cheeseburgers <= 68, "protein_resource_constraint")
model.addConstr(8 * cheeseburgers <= 156, "tastiness_resource_constraint")



# Optimize model
model.optimize()

# Print results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal objective value: {model.objVal}")
    print(f"Cheeseburgers: {cheeseburgers.x}")
    print(f"Oreos: {oreos.x}")
    print(f"Bananas: {bananas.x}")
elif model.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {model.status}")

```