```json
{
  "sym_variables": [
    ("x0", "apple pies"),
    ("x1", "apples"),
    ("x2", "oreos"),
    ("x3", "bowls of cereal")
  ],
  "objective_function": "8*x0 + 4*x1 + 7*x2 + 3*x3",
  "constraints": [
    "18*x1 + 18*x2 >= 15",
    "6*x0 + 18*x1 >= 36",
    "6*x0 + 21*x3 >= 29",
    "6*x0 + 18*x2 >= 18",
    "6*x0 + 18*x1 + 18*x2 >= 27",
    "6*x0 + 18*x2 + 21*x3 >= 27",
    "18*x1 + 18*x2 + 21*x3 >= 27",
    "6*x0 + 18*x1 + 18*x2 >= 41",
    "6*x0 + 18*x2 + 21*x3 >= 41",
    "18*x1 + 18*x2 + 21*x3 >= 41",
    "6*x0 + 18*x1 + 18*x2 >= 23",
    "6*x0 + 18*x2 + 21*x3 >= 23",
    "18*x1 + 18*x2 + 21*x3 >= 23",
    "6*x0 + 18*x1 + 18*x2 + 21*x3 >= 23",
    "12*x0 + 15*x1 >= 18",
    "15*x1 + 11*x2 >= 17",
    "12*x0 + 11*x2 >= 36",
    "11*x2 + 4*x3 >= 20",
    "12*x0 + 15*x1 + 4*x3 >= 37",
    "12*x0 + 15*x1 + 11*x2 >= 37",
    "12*x0 + 15*x1 + 4*x3 >= 24",
    "12*x0 + 15*x1 + 11*x2 >= 24",
    "12*x0 + 15*x1 + 11*x2 + 4*x3 >= 24",
    "-9*x2 + 1*x3 >= 0",
    "6*x0 + 18*x1 + 18*x2 <= 72",
    "6*x0 + 18*x1 + 21*x3 <= 74",
    "18*x1 + 18*x2 + 21*x3 <= 50",
    "6*x0 + 18*x2 + 21*x3 <= 158",
    "12*x0 + 15*x1 <= 102",
    "15*x1 + 11*x2 <= 155",
    "12*x0 + 11*x2 + 4*x3 <= 116",
    "6*x0 + 18*x1 <= 175",
    "12*x0 + 15*x1 <= 157",
    "18*x2 <= 175",
    "11*x2 <= 157",
    "21*x3 <= 175",
    "4*x3 <= 157"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
apple_pies = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="apple_pies")
apples = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="apples")
oreos = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="oreos")
bowls_of_cereal = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="bowls_of_cereal")


# Set objective function
m.setObjective(8*apple_pies + 4*apples + 7*oreos + 3*bowls_of_cereal, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(18*apples + 18*oreos >= 15)
m.addConstr(6*apple_pies + 18*apples >= 36)
m.addConstr(6*apple_pies + 21*bowls_of_cereal >= 29)
m.addConstr(6*apple_pies + 18*oreos >= 18)
m.addConstr(6*apple_pies + 18*apples + 18*oreos >= 27)
m.addConstr(6*apple_pies + 18*oreos + 21*bowls_of_cereal >= 27)
m.addConstr(18*apples + 18*oreos + 21*bowls_of_cereal >= 27)
m.addConstr(6*apple_pies + 18*apples + 18*oreos >= 41)
m.addConstr(6*apple_pies + 18*oreos + 21*bowls_of_cereal >= 41)
m.addConstr(18*apples + 18*oreos + 21*bowls_of_cereal >= 41)
m.addConstr(6*apple_pies + 18*apples + 18*oreos >= 23)
m.addConstr(6*apple_pies + 18*oreos + 21*bowls_of_cereal >= 23)
m.addConstr(18*apples + 18*oreos + 21*bowls_of_cereal >= 23)
m.addConstr(6*apple_pies + 18*apples + 18*oreos + 21*bowls_of_cereal >= 23)
m.addConstr(12*apple_pies + 15*apples >= 18)
m.addConstr(15*apples + 11*oreos >= 17)
m.addConstr(12*apple_pies + 11*oreos >= 36)
m.addConstr(11*oreos + 4*bowls_of_cereal >= 20)
m.addConstr(12*apple_pies + 15*apples + 4*bowls_of_cereal >= 37)
m.addConstr(12*apple_pies + 15*apples + 11*oreos >= 37)
m.addConstr(12*apple_pies + 15*apples + 4*bowls_of_cereal >= 24)
m.addConstr(12*apple_pies + 15*apples + 11*oreos >= 24)
m.addConstr(12*apple_pies + 15*apples + 11*oreos + 4*bowls_of_cereal >= 24)
m.addConstr(-9*oreos + 1*bowls_of_cereal >= 0)
m.addConstr(6*apple_pies + 18*apples + 18*oreos <= 72)
m.addConstr(6*apple_pies + 18*apples + 21*bowls_of_cereal <= 74)
m.addConstr(18*apples + 18*oreos + 21*bowls_of_cereal <= 50)
m.addConstr(6*apple_pies + 18*oreos + 21*bowls_of_cereal <= 158)
m.addConstr(12*apple_pies + 15*apples <= 102)
m.addConstr(15*apples + 11*oreos <= 155)
m.addConstr(12*apple_pies + 11*oreos + 4*bowls_of_cereal <= 116)


# Resource Constraints
m.addConstr(6*apple_pies + 18*apples + 18*oreos + 21*bowls_of_cereal <= 175) # fat
m.addConstr(12*apple_pies + 15*apples + 11*oreos + 4*bowls_of_cereal <= 157) # fiber


# 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 problem is infeasible.")
else:
    print("The problem could not be solved to optimality.")

```