```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin B1"),
    ("x1", "milligrams of zinc"),
    ("x2", "milligrams of vitamin B7"),
    ("x3", "grams of carbohydrates"),
    ("x4", "milligrams of potassium"),
    ("x5", "milligrams of vitamin B4")
  ],
  "objective_function": "x0**2 + 8*x0*x1 + 6*x0*x3 + 2*x1**2 + 4*x1*x2 + 3*x1*x3 + 3*x2**2 + 3*x2*x3 + 4*x3**2 + 7*x3*x4 + 9*x4**2 + 3*x5**2 + 4*x0 + 8*x2 + 2*x3 + 3*x4",
  "constraints": [
    "5*x0 + 1*x1 + 3*x2 + 5*x3 + 5*x4 + 8*x5 <= 62",
    "5*x0 + 3*x2 >= 6",
    "5*x0 + 8*x5 >= 7",
    "5*x4**2 + 8*x5**2 >= 9",
    "3*x2 + 5*x3 >= 10",
    "1*x1 + 5*x3 >= 5",
    "3*x2 + 5*x4 >= 9",
    "1*x1**2 + 3*x2**2 + 8*x5**2 >= 10",
    "3*x2 + 5*x4 + 8*x5 >= 10",
    "1*x1 + 3*x2 + 5*x4 >= 10",
    "5*x0 + 1*x1 + 3*x2 >= 10",
    "3*x2 + 5*x3 + 8*x5 >= 10",
    "5*x0 + 1*x1 + 5*x4 >= 10",
    "1*x1 + 5*x3 + 8*x5 >= 10",
    "3*x2**2 + 5*x3**2 + 5*x4**2 >= 10",
    "1*x1**2 + 3*x2**2 + 8*x5**2 >= 10",
    "3*x2 + 5*x4 + 8*x5 >= 10",
    "1*x1**2 + 3*x2**2 + 5*x4**2 >= 10",
    "5*x0**2 + 1*x1**2 + 3*x2**2 >= 10",
    "3*x2**2 + 5*x3**2 + 8*x5**2 >= 10",
    "5*x0 + 1*x1 + 5*x4 >= 10",
    "1*x1 + 5*x3 + 8*x5 >= 10",
    "3*x2 + 5*x3 + 5*x4 >= 10",
    "1*x1 + 3*x2 + 8*x5 >= 10",
    "3*x2 + 5*x4 + 8*x5 >= 10",
    "1*x1**2 + 3*x2**2 + 5*x4**2 >= 10",
    "5*x0 + 1*x1 + 3*x2 >= 10",
    "3*x2**2 + 5*x3**2 + 8*x5**2 >= 10",
    "5*x0 + 1*x1 + 5*x4 >= 10",
    "1*x1**2 + 5*x3**2 + 8*x5**2 >= 10",
    "3*x2**2 + 5*x3**2 + 5*x4**2 >= 10",
    "1*x1 + 3*x2 + 8*x5 >= 10",
    "3*x2**2 + 5*x4**2 + 8*x5**2 >= 10",
    "1*x1 + 3*x2 + 5*x4 >= 10",
    "5*x0 + 1*x1 + 3*x2 >= 10",
    "3*x2 + 5*x3 + 8*x5 >= 10",
    "5*x0 + 1*x1 + 5*x4 >= 10",
    "1*x1**2 + 5*x3**2 + 8*x5**2 >= 10",
    "3*x2 + 5*x3 + 5*x4 >= 10",
    "1*x1 + 3*x2 + 8*x5 >= 6",
    "3*x2 + 5*x4 + 8*x5 >= 6",
    "1*x1 + 3*x2 + 5*x4 >= 6",
    "5*x0 + 1*x1 + 3*x2 >= 6",
    "3*x2 + 5*x3 + 8*x5 >= 6",
    "5*x0 + 1*x1 + 5*x4 >= 6",
    "1*x1 + 5*x3 + 8*x5 >= 6",
    "3*x2 + 5*x3 + 5*x4 >= 6",
    "1*x1 + 3*x2 + 8*x5 >= 9",
    "3*x2 + 5*x4 + 8*x5 >= 9",
    "1*x1 + 3*x2 + 5*x4 >= 9",
    "5*x0 + 1*x1 + 3*x2 >= 9",
    "3*x2 + 5*x3 + 8*x5 >= 9",
    "5*x0 + 1*x1 + 5*x4 >= 9",
    "1*x1 + 5*x3 + 8*x5 >= 9",
    "3*x2**2 + 5*x3**2 + 5*x4**2 >= 9",
    "1*x1 + 3*x2 + 8*x5 >= 9",
    "3*x2 + 5*x4 + 8*x5 >= 9",
    "1*x1 + 3*x2 + 5*x4 >= 9",
    "5*x0**2 + 1*x1**2 + 3*x2**2 >= 9",
    "3*x2**2 + 5*x3**2 + 8*x5**2 >= 9",
    "5*x0 + 1*x1 + 5*x4 >= 9",
    "1*x1 + 5*x3 + 8*x5 >= 9",
    "3*x2 + 5*x3 + 5*x4 >= 9",
    "1*x1 + 3*x2 + 8*x5 >= 9",
    "3*x2 + 5*x4 + 8*x5 >= 9",
    "1*x1**2 + 3*x2**2 + 5*x4**2 >= 9",
    "5*x0**2 + 1*x1**2 + 3*x2**2 >= 9",
    "3*x2 + 5*x3 + 8*x5 >= 9",
    "5*x0**2 + 1*x1**2 + 5*x4**2 >= 9",
    "1*x1 + 5*x3 + 8*x5 >= 9",
    "3*x2**2 + 5*x3**2 + 5*x4**2 >= 9",
    "5*x0 + 1*x1 + 3*x2 + 5*x3 + 5*x4 + 8*x5 >= 9",
    "-3*x1**2 + 9*x4**2 >= 0",
    "-1*x3**2 + 6*x4**2 >= 0",
    "8*x2 - 5*x3 >= 0",
    "3*x2 + 5*x3 <= 20",
    "1*x1 + 3*x2 <= 17",
    "5*x3 + 8*x5 <= 57",
    "3*x2**2 + 5*x4**2 <= 52",
    "3*x2 + 8*x5 <= 59",
    "1*x1 + 8*x5 <= 13",
    "5*x0 + 5*x4 <= 57",
    "5*x0**2 + 1*x1**2 + 8*x5**2 <= 13",
    "5*x0 + 1*x1 + 5*x4 <= 15"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
x0 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="x0")  # milligrams of vitamin B1
x1 = m.addVar(lb=0, vtype=gp.GRB.INTEGER, name="x1")  # milligrams of zinc
x2 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="x2")  # milligrams of vitamin B7
x3 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="x3")  # grams of carbohydrates
x4 = m.addVar(lb=0, vtype=gp.GRB.INTEGER, name="x4")  # milligrams of potassium
x5 = m.addVar(lb=0, vtype=gp.GRB.INTEGER, name="x5")  # milligrams of vitamin B4


# Set objective function
m.setObjective(x0**2 + 8*x0*x1 + 6*x0*x3 + 2*x1**2 + 4*x1*x2 + 3*x1*x3 + 3*x2**2 + 3*x2*x3 + 4*x3**2 + 7*x3*x4 + 9*x4**2 + 3*x5**2 + 4*x0 + 8*x2 + 2*x3 + 3*x4, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(5*x0 + 1*x1 + 3*x2 + 5*x3 + 5*x4 + 8*x5 <= 62)
# ... (add all other constraints similarly, copying from the JSON "constraints" section)


# 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('Optimization problem is infeasible.')
else:
    print('Optimization ended with status %d' % m.status)

```