```json
{
  "sym_variables": [
    ("x0", "potatoes"),
    ("x1", "rotisserie chickens"),
    ("x2", "corn cobs")
  ],
  "objective_function": "6.51*x0**2 + 6.3*x0*x1 + 8.57*x1**2 + 3.48*x1*x2 + 7.18*x2**2 + 4.64*x1 + 3.49*x2",
  "constraints": [
    "5.32*x0 + 5.14*x1 + 0.57*x2 <= 105",
    "-x1 + 10*x2 >= 0",
    "5.32*x0 + 0.57*x2 <= 47",
    "(5.14*x1)**2 + (0.57*x2)**2 <= 67",
    "(5.32*x0)**2 + (5.14*x1)**2 <= 74",
    "5.32*x0 + 5.14*x1 + 0.57*x2 <= 74"
  ]
}
```

```python
import gurobipy as gp
from gurobipy import GRB

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

# Create variables
potatoes = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="potatoes")
rotisserie_chickens = m.addVar(lb=0, vtype=GRB.INTEGER, name="rotisserie_chickens")
corn_cobs = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="corn_cobs")


# Set objective function
m.setObjective(6.51*potatoes**2 + 6.3*potatoes*rotisserie_chickens + 8.57*rotisserie_chickens**2 + 3.48*rotisserie_chickens*corn_cobs + 7.18*corn_cobs**2 + 4.64*rotisserie_chickens + 3.49*corn_cobs, GRB.MAXIMIZE)

# Add constraints
m.addConstr(5.32*potatoes + 5.14*rotisserie_chickens + 0.57*corn_cobs <= 105, "c0")
m.addConstr(-rotisserie_chickens + 10*corn_cobs >= 0, "c1")
m.addConstr(5.32*potatoes + 0.57*corn_cobs <= 47, "c2")
m.addConstr((5.14*rotisserie_chickens)**2 + (0.57*corn_cobs)**2 <= 67, "c3")
m.addConstr((5.32*potatoes)**2 + (5.14*rotisserie_chickens)**2 <= 74, "c4")
m.addConstr(5.32*potatoes + 5.14*rotisserie_chickens + 0.57*corn_cobs <= 74, "c5")



# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    print('potatoes:', potatoes.x)
    print('rotisserie_chickens:', rotisserie_chickens.x)
    print('corn_cobs:', corn_cobs.x)
elif m.status == GRB.INFEASIBLE:
    print('The model is infeasible.')
else:
    print('Optimization ended with status %d' % m.status)

```