```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin B7"),
    ("x1", "milligrams of vitamin B12"),
    ("x2", "milligrams of vitamin B9"),
    ("x3", "grams of fiber"),
    ("x4", "milligrams of iron"),
    ("x5", "milligrams of vitamin D"),
    ("x6", "milligrams of vitamin A")
  ],
  "objective_function": "2*x0*x1 + 6*x0*x4 + 7*x1*x3 + 9*x1*x5 + 3*x1*x6 + x2*x6 + 5*x3*x5 + 4*x4**2 + 5*x4*x6 + x5*x6 + 4*x0 + 2*x1 + x2 + 6*x3 + 9*x4 + 9*x6",
  "constraints": [
    "11*x0 + 9*x1 + 11*x2 + 9*x3 + 10*x4 + 3*x5 + 9*x6 <= 132",
    "8*x0 + 4*x1 + 10*x2 + 5*x3 + 2*x4 + 8*x5 + 6*x6 <= 336",
    "x0 + 2*x1 + 6*x2 + 6*x3 + 11*x4 + 5*x5 + 11*x6 <= 199",
    "9*x0 + 10*x1 + 7*x2 + 9*x3 + x4 + 6*x5 + 11*x6 <= 149",
    "9*x3**2 + 9*x6**2 >= 17",
    "9*x1 + 11*x2 >= 15",
    "9*x1 + 10*x4 >= 12",
    "11*x0**2 + 9*x3**2 >= 9",
    "11*x0 + 3*x5 >= 18",
    "11*x0 + 11*x2 >= 15",
    "9*x3 + 3*x5 >= 12",
    "9*x1 + 9*x3 >= 13",
    "3*x5 + 9*x6 >= 16",
    "9*x1**2 + 3*x5**2 >= 6",
    "9*x1 + 10*x4 + 3*x5 >= 18",
    "11*x0 + 9*x1 + 11*x2 + 9*x3 + 10*x4 + 3*x5 + 9*x6 >= 18",
    "8*x0 + 10*x2 >= 18",
    "4*x1 + 10*x2 >= 48",
    "8*x0 + 4*x1 >= 21",
    "4*x1 + 6*x6 >= 38",
    "8*x0 + 6*x6 >= 17",
    "8*x0 + 8*x5 + 6*x6 >= 37",
    "8*x0 + 4*x1 + 10*x2 + 5*x3 + 2*x4 + 8*x5 + 6*x6 >= 37",
    "6*x3 + 11*x6 >= 23",
    "x0 + 11*x6 >= 10",
    "2*x1**2 + 11*x6**2 >= 10",
    "2*x1**2 + 6*x3**2 >= 12",
    "5*x5 + 11*x6 >= 24",
    "11*x4 + 5*x5 >= 10",
    "6*x2 + 6*x3 >= 14",
    "x0**2 + 5*x5**2 >= 10",
    "6*x2 + 11*x6 >= 27",
    "x0**2 + 11*x4**2 >= 28",
    "2*x1**2 + 5*x5**2 >= 11",
    "6*x2 + 11*x4 >= 25",
    "6*x3 + 5*x5 >= 9",
    "x0 + 6*x3 >= 24",
    "x0**2 + 2*x1**2 + 6*x3**2 >= 24",
    "x0 + 6*x3 + 11*x6 >= 24",
    "x0 + 2*x1 + 6*x2 >= 24",
    "x0**2 + 6*x3**2 + 5*x5**2 >= 24",
    "2*x1 + 6*x3 + 11*x6 >= 24",
    "6*x2 + 11*x4 + 5*x5 >= 24",
    "6*x2**2 + 5*x5**2 + 11*x6**2 >= 24",
    "x0 + 6*x2 + 5*x5 >= 24",
    "2*x1 + 11*x4 + 5*x5 >= 24",
    "-x1 + 9*x2 >= 0",
    "7*x4 - 2*x6 >= 0",
    "-2*x2 + 7*x3 + 4*x5 >= 0",
    "-2*x0 + 10*x2 + 2*x6 >= 0",
    "11*x0 + 10*x4 <= 37",
    "11*x0 + 9*x1 <= 122",
    "10*x4 + 3*x5 <= 66",
    "9*x1 + 10*x4 <= 126",
    "9*x1 + 3*x5 <= 90",
    "10*x4 + 9*x6 <= 82",
    "11*x2 + 10*x4 <= 57",
    "9*x3 + 9*x6 <= 96",
    "9*x3**2 + 10*x4**2 <= 129",
    "11*x0 + 9*x6 <= 34",
    "9*x1 + 9*x6 <= 96",
    "11*x2 + 3*x5 <= 88",
    "9*x1**2 + 11*x2**2 <= 127",
    "11*x2 + 10*x4 + 3*x5 <= 47",
    "11*x0 + 9*x1 + 3*x5 <= 81",
    "9*x1**2 + 11*x2**2 + 3*x5**2 <= 130",
    "11*x2 + 9*x3 + 9*x6 <= 89",
    "11*x0 + 10*x4 + 9*x6 <= 108",
    "4*x1**2 + 8*x5**2 <= 160",
    "2*x4 + 6*x6 <= 80",
    "8*x0**2 + 6*x6**2 <= 172",
    "4*x1 + 10*x2 <= 86",
    "8*x0**2 + 4*x1**2 + 5*x3**2 <= 130",
    "4*x1 + 10*x2 + 6*x6 <= 237",
    "8*x0**2 + 10*x2**2 + 6*x6**2 <= 117",
    "8*x0 + 4*x1 + 2*x4 <= 112",
    "8*x0**2 + 10*x2**2 + 8*x5**2 <= 253",
    "10*x2 + 8*x5 + 6*x6 <= 285"
    ]
}
```

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

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

# Create variables
x0 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="x0")
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="x1")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="x2")
x3 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="x3")
x4 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="x4")
x5 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="x5")
x6 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="x6")


# Set objective function
m.setObjective(2*x0*x1 + 6*x0*x4 + 7*x1*x3 + 9*x1*x5 + 3*x1*x6 + x2*x6 + 5*x3*x5 + 4*x4**2 + 5*x4*x6 + x5*x6 + 4*x0 + 2*x1 + x2 + 6*x3 + 9*x4 + 9*x6, GRB.MINIMIZE)

# Add constraints
m.addConstr(11*x0 + 9*x1 + 11*x2 + 9*x3 + 10*x4 + 3*x5 + 9*x6 <= 132)
m.addConstr(8*x0 + 4*x1 + 10*x2 + 5*x3 + 2*x4 + 8*x5 + 6*x6 <= 336)
m.addConstr(x0 + 2*x1 + 6*x2 + 6*x3 + 11*x4 + 5*x5 + 11*x6 <= 199)
m.addConstr(9*x0 + 10*x1 + 7*x2 + 9*x3 + x4 + 6*x5 + 11*x6 <= 149)
# ... (add all other constraints similarly)


# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    for v in m.getVars():
        print('%s %g' % (v.varName, v.x))
elif m.status == GRB.INFEASIBLE:
    print('Optimization problem is infeasible.')
else:
    print('Optimization ended with status %d' % m.status)

```