```json
{
  "sym_variables": [
    ("x0", "oranges"),
    ("x1", "bowls of pasta"),
    ("x2", "cornichons")
  ],
  "objective_function": "5.56 * x0 + 8.24 * x1 + 7.5 * x2",
  "constraints": [
    "1 * x0 + 4 * x1 >= 32",
    "1 * x0 + 2 * x2 >= 26",
    "1 * x0 + 4 * x1 + 2 * x2 >= 26",
    "2 * x1 + 7 * x2 >= 8",
    "5 * x0 + 2 * x1 >= 6",
    "5 * x0 + 2 * x1 + 7 * x2 >= 6",
    "1 * x1 + 8 * x2 >= 40",
    "7 * x0 + 1 * x1 >= 35",
    "7 * x0 + 1 * x1 + 8 * x2 >= 35",
    "-1 * x0 + 7 * x1 >= 0",
    "5 * x0 + 2 * x1 + 7 * x2 <= 35",
    "x0 + 5*x1 + 7 * x2 <= 149",
    "1 * x0 + 4 * x1 + 2 * x2 <= 116",
    "5 * x0 + 2 * x1 + 7 * x2 <= 42"

  ]
}
```

```python
import gurobipy as gp

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

# Create variables
oranges = m.addVar(lb=0, name="oranges")
pasta = m.addVar(lb=0, name="bowls_of_pasta")
cornichons = m.addVar(lb=0, name="cornichons")


# Set objective function
m.setObjective(5.56 * oranges + 8.24 * pasta + 7.5 * cornichons, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(1 * oranges + 4 * pasta >= 32, "protein_constraint1")
m.addConstr(1 * oranges + 2 * cornichons >= 26, "protein_constraint2")
m.addConstr(1 * oranges + 4 * pasta + 2 * cornichons >= 26, "protein_constraint3")

m.addConstr(2 * pasta + 7 * cornichons >= 8, "carbohydrate_constraint1")
m.addConstr(5 * oranges + 2 * pasta >= 6, "carbohydrate_constraint2")
m.addConstr(5 * oranges + 2 * pasta + 7 * cornichons >= 6, "carbohydrate_constraint3")
m.addConstr(5 * oranges + 2 * pasta + 7 * cornichons <= 35, "carbohydrate_constraint4")


m.addConstr(1 * pasta + 8 * cornichons >= 40, "fat_constraint1")
m.addConstr(7 * oranges + 1 * pasta >= 35, "fat_constraint2")
m.addConstr(7 * oranges + 1 * pasta + 8 * cornichons >= 35, "fat_constraint3")

m.addConstr(-1 * oranges + 7 * pasta >= 0, "proportion_constraint")

# Resource Constraints from the context
m.addConstr(1 * oranges + 4 * pasta + 2 * cornichons <= 116, "protein_resource_constraint")
m.addConstr(5 * oranges + 2 * pasta + 7 * cornichons <= 42, "carbohydrate_resource_constraint")
m.addConstr(7 * oranges + 1 * pasta + 8 * cornichons <= 149, "fat_resource_constraint")



# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    print('oranges:', oranges.x)
    print('pasta:', pasta.x)
    print('cornichons:', cornichons.x)
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print("Optimization ended with status:", m.status)

```