```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin K"),
    ("x1", "milligrams of vitamin B6"),
    ("x2", "milligrams of vitamin B3"),
    ("x3", "grams of carbohydrates"),
    ("x4", "milligrams of vitamin E")
  ],
  "objective_function": "7*x0*x1 + 6*x1*x2 + x1*x3 + 7*x1*x4 + 8*x2*x3 + x2*x4 + 7*x3*x4 + 5*x1 + 8*x2 + 9*x3",
  "constraints": [
    "9.81*x1 + 10.82*x4 >= 92",
    "6.74*x0^2 + 9.66*x2^2 >= 66",
    "6.74*x0^2 + 9.81*x1^2 >= 56",
    "6.74*x0 + 10.82*x4 >= 56",
    "6.74*x0 + 9.81*x1 + 16.1*x3 >= 114",
    "6.74*x0 + 16.1*x3 + 10.82*x4 >= 114",
    "9.81*x1 + 9.66*x2 + 10.82*x4 >= 114",
    "9.81*x1 + 16.1*x3 + 10.82*x4 >= 114",
    "6.74*x0 + 9.66*x2 + 16.1*x3 >= 114",
    "9.81*x1 + 9.66*x2 + 16.1*x3 >= 114",
    "6.74*x0^2 + 9.66*x2^2 + 10.82*x4^2 >= 114",
    "6.74*x0^2 + 9.81*x1^2 + 16.1*x3^2 >= 122",
    "6.74*x0 + 16.1*x3 + 10.82*x4 >= 122",
    "9.81*x1 + 9.66*x2 + 10.82*x4 >= 122",
    "9.81*x1^2 + 16.1*x3^2 + 10.82*x4^2 >= 122",
    "6.74*x0 + 9.66*x2 + 16.1*x3 >= 122",
    "9.81*x1^2 + 9.66*x2^2 + 16.1*x3^2 >= 122",
    "6.74*x0 + 9.66*x2 + 10.82*x4 >= 122",
    "6.74*x0 + 9.81*x1 + 16.1*x3 >= 99",
    "6.74*x0^2 + 16.1*x3^2 + 10.82*x4^2 >= 99",
    "9.81*x1^2 + 9.66*x2^2 + 10.82*x4^2 >= 99",
    "9.81*x1 + 16.1*x3 + 10.82*x4 >= 99",
    "6.74*x0 + 9.66*x2 + 16.1*x3 >= 99",
    "9.81*x1 + 9.66*x2 + 16.1*x3 >= 99",
    "6.74*x0^2 + 9.66*x2^2 + 10.82*x4^2 >= 99",
    "6.74*x0^2 + 9.81*x1^2 + 16.1*x3^2 >= 126",
    "6.74*x0 + 16.1*x3 + 10.82*x4 >= 126",
    "9.81*x1 + 9.66*x2 + 10.82*x4 >= 126",
    "9.81*x1 + 16.1*x3 + 10.82*x4 >= 126",
    "6.74*x0 + 9.66*x2 + 16.1*x3 >= 126",
    "9.81*x1 + 9.66*x2 + 16.1*x3 >= 126",
    "6.74*x0 + 9.66*x2 + 10.82*x4 >= 126",
    "6.74*x0 + 9.81*x1 + 16.1*x3 >= 145",
    "6.74*x0 + 16.1*x3 + 10.82*x4 >= 145",
    "9.81*x1 + 9.66*x2 + 10.82*x4 >= 145",
    "9.81*x1 + 16.1*x3 + 10.82*x4 >= 145",
    "6.74*x0 + 9.66*x2 + 16.1*x3 >= 145",
    "9.81*x1 + 9.66*x2 + 16.1*x3 >= 145",
    "6.74*x0^2 + 9.66*x2^2 + 10.82*x4^2 >= 145",
    "6.74*x0 + 9.81*x1 + 16.1*x3 >= 124",
    "6.74*x0 + 16.1*x3 + 10.82*x4 >= 124",
    "9.81*x1^2 + 9.66*x2^2 + 10.82*x4^2 >= 124",
    "9.81*x1^2 + 16.1*x3^2 + 10.82*x4^2 >= 124",
    "6.74*x0 + 9.66*x2 + 16.1*x3 >= 124",
    "9.81*x1^2 + 9.66*x2^2 + 16.1*x3^2 >= 124",
    "6.74*x0 + 9.66*x2 + 10.82*x4 >= 124",
    "6.74*x0 + 9.81*x1 + 16.1*x3 >= 118",
    "6.74*x0 + 16.1*x3 + 10.82*x4 >= 118",
    "9.81*x1^2 + 9.66*x2^2 + 10.82*x4^2 >= 118",
    "9.81*x1^2 + 16.1*x3^2 + 10.82*x4^2 >= 118",
    "6.74*x0^2 + 9.66*x2^2 + 16.1*x3^2 >= 118",
    "9.81*x1^2 + 9.66*x2^2 + 16.1*x3^2 >= 118",
    "6.74*x0^2 + 9.66*x2^2 + 10.82*x4^2 >= 118",
    "6.74*x0 + 9.81*x1 + 9.66*x2 + 16.1*x3 + 10.82*x4 >= 118",
    "20.41*x1^2 + 22.06*x4^2 >= 96",
    "3.13*x0^2 + 16.06*x3^2 >= 88",
    "16.06*x3^2 + 22.06*x4^2 >= 47",
    "3.13*x0 + 22.06*x4 >= 59",
    "20.41*x1 + 16.06*x3 >= 73",
    "20.41*x1 + 9.36*x2 >= 45",
    "3.13*x0 + 20.41*x1 + 9.36*x2 + 16.06*x3 + 22.06*x4 >= 45",
    "3*x0 - 4*x3 >= 0",
    "-7*x2^2 + 8*x3^2 >= 0",
    "9.66*x2 + 16.1*x3 <= 210",
    "16.1*x3 + 10.82*x4 <= 295",
    "6.74*x0 + 9.81*x1 + 10.82*x4 <= 208",
    "9.81*x1^2 + 9.66*x2^2 + 10.82*x4^2 <= 700",
    "9.66*x2 + 16.1*x3 + 10.82*x4 <= 383",
    "6.74*x0 + 9.66*x2 + 10.82*x4 <= 726",
    "3.13*x0 + 22.06*x4 <= 449",
    "9.36*x2 + 22.06*x4 <= 494",
    "20.41*x1 + 22.06*x4 <= 382",
    "x0 >= 0",
    "x1 >= 0",
    "x2 >= 0",
    "x3 >= 0",
    "x4 >= 0"
  ]
}
```

```python
import gurobipy as gp

try:
    # 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 K
    x1 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="x1")  # milligrams of vitamin B6
    x2 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="x2")  # milligrams of vitamin B3
    x3 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="x3")  # grams of carbohydrates
    x4 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="x4")  # milligrams of vitamin E


    # Set objective function
    m.setObjective(7*x0*x1 + 6*x1*x2 + x1*x3 + 7*x1*x4 + 8*x2*x3 + x2*x4 + 7*x3*x4 + 5*x1 + 8*x2 + 9*x3, gp.GRB.MINIMIZE)

    # Add constraints
    m.addConstr(9.81*x1 + 10.82*x4 >= 92)
    m.addConstr(6.74*x0**2 + 9.66*x2**2 >= 66)
    m.addConstr(6.74*x0**2 + 9.81*x1**2 >= 56)
    m.addConstr(6.74*x0 + 10.82*x4 >= 56)
    # ... (add all other constraints similarly)
    m.addConstr(3*x0 - 4*x3 >= 0)
    m.addConstr(-7*x2**2 + 8*x3**2 >= 0)
    # ... (add all other constraints similarly)
    m.addConstr(20.41*x1 + 22.06*x4 <= 382)


    # Optimize model
    m.optimize()

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


except gp.GurobiError as e:
    print('Error code ' + str(e.errno) + ': ' + str(e))

except AttributeError:
    print('Encountered an attribute error')
```