```json
{
  "sym_variables": [
    ("x0", "milligrams of calcium"),
    ("x1", "milligrams of vitamin B3"),
    ("x2", "grams of fiber"),
    ("x3", "milligrams of vitamin D"),
    ("x4", "milligrams of vitamin E"),
    ("x5", "milligrams of vitamin A")
  ],
  "objective_function": "9*x0**2 + 3*x0*x1 + 7*x0*x2 + x0*x3 + 5*x1**2 + 6*x1*x2 + 6*x1*x4 + 8*x2**2 + x2*x3 + 7*x2*x4 + x2*x5 + 6*x3**2 + x3*x4 + 4*x3*x5 + 2*x4*x5 + 3*x5**2 + 5*x1 + 2*x2 + 5*x3 + 3*x4",
  "constraints": [
    "28*x0 + 22*x2 >= 59",
    "28*x0**2 + 13*x1**2 + 20*x4**2 >= 56",
    "28*x0**2 + 22*x2**2 + 31*x5**2 >= 56",
    "28*x0 + 13*x1 + 20*x4 >= 70",
    "28*x0 + 22*x2 + 31*x5 >= 70",
    "3*x2 + 11*x5 >= 31",
    "23*x0**2 + 29*x4**2 >= 45",
    "16*x1 + 29*x4 >= 31",
    "16*x1 + 3*x2 >= 83",
    "16*x1**2 + 11*x5**2 >= 50",
    "17*x1**2 + 18*x2**2 + 27*x3**2 >= 48",
    "28*x0**2 + 13*x1**2 <= 85",
    "28*x0**2 + 31*x5**2 <= 210",
    "22*x2 + 31*x5 <= 400",
    "22*x2**2 + 5*x3**2 <= 158",
    "22*x2 + 20*x4 <= 290",
    "13*x1 + 22*x2 <= 189",
    "28*x0 + 20*x4 <= 350",
    "28*x0 + 5*x3 <= 126",
    "20*x4 + 31*x5 <= 115",
    "13*x1 + 5*x3 <= 330",
    "28*x0 + 13*x1 + 22*x2 + 5*x3 + 20*x4 + 31*x5 <= 330",
    "16*x1**2 + 11*x5**2 <= 244",
    "23*x0**2 + 16*x1**2 + 11*x5**2 <= 214",
    "3*x2 + 27*x3 + 11*x5 <= 354",
    "23*x0 + 16*x1 + 3*x2 + 27*x3 + 29*x4 + 11*x5 <= 354",
    "8*x0 + 18*x2 <= 550",
    "22*x3 + 28*x4 <= 404",
    "8*x0**2 + 27*x5**2 <= 297",
    "28*x4 + 27*x5 <= 188",
    "18*x2**2 + 22*x3**2 <= 394",
    "17*x1**2 + 22*x3**2 <= 410",
    "8*x0**2 + 17*x1**2 <= 104",
    "8*x0 + 17*x1 + 22*x3 <= 192",
    "8*x0 + 17*x1 + 27*x5 <= 567",
    "17*x1 + 18*x2 + 27*x5 <= 193",
    "8*x0**2 + 17*x1**2 + 28*x4**2 <= 462",
    "17*x1 + 18*x2 + 28*x4 <= 528",
    "8*x0 + 18*x2 + 28*x4 <= 574",
    "8*x0 + 28*x4 + 27*x5 <= 577",
    "17*x1 + 28*x4 + 27*x5 <= 530",
    "8*x0 + 17*x1 + 18*x2 + 22*x3 + 28*x4 + 27*x5 <= 530"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
x0 = m.addVar(vtype=gp.GRB.INTEGER, name="x0")  # milligrams of calcium
x1 = m.addVar(vtype=gp.GRB.INTEGER, name="x1")  # milligrams of vitamin B3
x2 = m.addVar(vtype=gp.GRB.CONTINUOUS, name="x2") # grams of fiber
x3 = m.addVar(vtype=gp.GRB.INTEGER, name="x3")  # milligrams of vitamin D
x4 = m.addVar(vtype=gp.GRB.INTEGER, name="x4")  # milligrams of vitamin E
x5 = m.addVar(vtype=gp.GRB.CONTINUOUS, name="x5") # milligrams of vitamin A


# Set objective function
m.setObjective(9*x0**2 + 3*x0*x1 + 7*x0*x2 + x0*x3 + 5*x1**2 + 6*x1*x2 + 6*x1*x4 + 8*x2**2 + x2*x3 + 7*x2*x4 + x2*x5 + 6*x3**2 + x3*x4 + 4*x3*x5 + 2*x4*x5 + 3*x5**2 + 5*x1 + 2*x2 + 5*x3 + 3*x4, gp.GRB.MAXIMIZE)

# Add constraints
resource_constraints = {
    'r0': {'upper_bound': 435, 'coefficients': [28, 13, 22, 5, 20, 31]},
    'r1': {'upper_bound': 543, 'coefficients': [23, 16, 3, 27, 29, 11]},
    'r2': {'upper_bound': 580, 'coefficients': [8, 17, 18, 22, 28, 27]}
}

for r, data in resource_constraints.items():
    m.addConstr(gp.quicksum(data['coefficients'][i] * vars()[f'x{i}'] for i in range(6)) <= data['upper_bound'], name=f'{r}_constraint')


m.addConstr(28*x0 + 22*x2 >= 59)
m.addConstr(28*x0**2 + 13*x1**2 + 20*x4**2 >= 56)
m.addConstr(28*x0**2 + 22*x2**2 + 31*x5**2 >= 56)
m.addConstr(28*x0 + 13*x1 + 20*x4 >= 70)
# ... (add all other constraints similarly)


# 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(f"Optimization ended with status {m.status}")

```