## Step 1: Define the symbolic representation of the variables
The variables are ['grams of fiber', 'grams of carbohydrates', 'milligrams of iron', 'milligrams of vitamin B2', 'milligrams of potassium']. Let's denote them as $x_0, x_1, x_2, x_3, x_4$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : grams of fiber
- $x_1$ : grams of carbohydrates
- $x_2$ : milligrams of iron
- $x_3$ : milligrams of vitamin B2
- $x_4$ : milligrams of potassium

## 3: Define the objective function in symbolic notation
The objective function to maximize is:
$8x_0^2 + 4x_0x_1 + x_0x_3 + 7x_0x_4 + 6x_1^2 + 6x_1x_3 + 7x_1x_4 + 4x_2^2 + 7x_2x_3 + x_2x_4 + 4x_3x_4 + 3x_4^2 + 8x_0 + 7x_2 + 3x_4$

## 4: List the constraints in symbolic notation
The constraints are:
- $x_0 \geq 0$ ( implicit, as grams of fiber cannot be negative)
- $x_1 \geq 0$ ( implicit, as grams of carbohydrates cannot be negative)
- $x_2 \geq 0$ ( implicit, as milligrams of iron cannot be negative)
- $x_3 \geq 0$ ( implicit, as milligrams of vitamin B2 cannot be negative)
- $x_4 \geq 0$ ( implicit, as milligrams of potassium cannot be negative)
- $x_2$ is an integer
- $11x_1 + 15x_2 + 22x_3 + 5x_4 \geq 33$ 
- $15^2x_2 + 22^2x_3 \geq 42$
- $1x_0 + 11x_1 \geq 43$
- $18x_1 + 4x_2 \geq 64$
- $14x_0 + 4x_2 \geq 61$
- $14x_0 + 11x_3 \geq 34$
- $14^2x_0 + 20^2x_4 \geq 25$
- $4x_2 + 20x_4 \geq 49$
- $18x_1 + 11x_3 \geq 60$
- $-9x_0^2 + 9x_1^2 + 5x_4^2 \geq 0$
- $22x_3 + 5x_4 \leq 147$
- $15^2x_2 + 5^2x_4 \leq 142$
- $1^2x_0 + 5^2x_4 \leq 258$
- $1^2x_0 + 22^2x_3 \leq 97$
- $11x_1 + 5x_4 \leq 131$
- $x_0 + 22x_3 + 5x_4 \leq 67$
- $x_0 + 11x_1 + 5x_4 \leq 56$
- $15^2x_2 + 22^2x_3 + 5^2x_4 \leq 205$
- $x_0 + x_1 + x_2 + x_3 + x_4 \leq 205$
- $4x_2 + 11x_3 \leq 160$
- $14x_0 + 4x_2 \leq 201$
- $11^2x_3 + 20^2x_4 \leq 108$
- $18x_1 + 4x_2 + 20x_4 \leq 322$
- $14x_0 + 18x_1 + 11x_3 \leq 67$
- $14x_0 + 18x_1 + 4x_2 + 11x_3 + 20x_4 \leq 67$

## 5: Create the Gurobi model and variables
```python
import gurobi

# Create a new model
m = gurobi.Model()

# Define the variables
x0 = m.addVar(name="x0", lb=0)  # grams of fiber
x1 = m.addVar(name="x1", lb=0)  # grams of carbohydrates
x2 = m.addVar(name="x2", lb=0, vtype=gurobi.GRB.INTEGER)  # milligrams of iron
x3 = m.addVar(name="x3", lb=0)  # milligrams of vitamin B2
x4 = m.addVar(name="x4", lb=0)  # milligrams of potassium

# Define the objective function
m.setObjective(8*x0**2 + 4*x0*x1 + x0*x3 + 7*x0*x4 + 6*x1**2 + 6*x1*x3 + 7*x1*x4 + 4*x2**2 + 7*x2*x3 + x2*x4 + 4*x3*x4 + 3*x4**2 + 8*x0 + 7*x2 + 3*x4, gurobi.GRB.MAXIMIZE)

# Add constraints
m.addConstr(11*x1 + 15*x2 + 22*x3 + 5*x4 >= 33)
m.addConstr(15**2*x2 + 22**2*x3 >= 42)
m.addConstr(x0 + 11*x1 >= 43)
m.addConstr(18*x1 + 4*x2 >= 64)
m.addConstr(14*x0 + 4*x2 >= 61)
m.addConstr(14*x0 + 11*x3 >= 34)
m.addConstr(14**2*x0 + 20**2*x4 >= 25)
m.addConstr(4*x2 + 20*x4 >= 49)
m.addConstr(18*x1 + 11*x3 >= 60)
m.addConstr(-9*x0**2 + 9*x1**2 + 5*x4**2 >= 0)
m.addConstr(22*x3 + 5*x4 <= 147)
m.addConstr(15**2*x2 + 5**2*x4 <= 142)
m.addConstr(x0 + 5**2*x4 <= 258)
m.addConstr(x0 + 22**2*x3 <= 97)
m.addConstr(11*x1 + 5*x4 <= 131)
m.addConstr(x0 + 22*x3 + 5*x4 <= 67)
m.addConstr(x0 + 11*x1 + 5*x4 <= 56)
m.addConstr(15**2*x2 + 22**2*x3 + 5**2*x4 <= 205)
m.addConstr(x0 + x1 + x2 + x3 + x4 <= 205)
m.addConstr(4*x2 + 11*x3 <= 160)
m.addConstr(14*x0 + 4*x2 <= 201)
m.addConstr(11**2*x3 + 20**2*x4 <= 108)
m.addConstr(18*x1 + 4*x2 + 20*x4 <= 322)
m.addConstr(14*x0 + 18*x1 + 11*x3 <= 67)
m.addConstr(14*x0 + 18*x1 + 4*x2 + 11*x3 + 20*x4 <= 67)

# Solve the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("x0: ", x0.varValue)
    print("x1: ", x1.varValue)
    print("x2: ", x2.varValue)
    print("x3: ", x3.varValue)
    print("x4: ", x4.varValue)
else:
    print("The model is infeasible")
```

## Step 6: Provide the symbolic representation in JSON format
```json
{
    "sym_variables": [
        ["x0", "grams of fiber"],
        ["x1", "grams of carbohydrates"],
        ["x2", "milligrams of iron"],
        ["x3", "milligrams of vitamin B2"],
        ["x4", "milligrams of potassium"]
    ],
    "objective_function": "8*x0^2 + 4*x0*x1 + x0*x3 + 7*x0*x4 + 6*x1^2 + 6*x1*x3 + 7*x1*x4 + 4*x2^2 + 7*x2*x3 + x2*x4 + 4*x3*x4 + 3*x4^2 + 8*x0 + 7*x2 + 3*x4",
    "constraints": [
        "11*x1 + 15*x2 + 22*x3 + 5*x4 >= 33",
        "15^2*x2 + 22^2*x3 >= 42",
        "x0 + 11*x1 >= 43",
        "18*x1 + 4*x2 >= 64",
        "14*x0 + 4*x2 >= 61",
        "14*x0 + 11*x3 >= 34",
        "14^2*x0 + 20^2*x4 >= 25",
        "4*x2 + 20*x4 >= 49",
        "18*x1 + 11*x3 >= 60",
        "-9*x0^2 + 9*x1^2 + 5*x4^2 >= 0",
        "22*x3 + 5*x4 <= 147",
        "15^2*x2 + 5^2*x4 <= 142",
        "x0 + 5^2*x4 <= 258",
        "x0 + 22^2*x3 <= 97",
        "11*x1 + 5*x4 <= 131",
        "x0 + 22*x3 + 5*x4 <= 67",
        "x0 + 11*x1 + 5*x4 <= 56",
        "15^2*x2 + 22^2*x3 + 5^2*x4 <= 205",
        "x0 + x1 + x2 + x3 + x4 <= 205",
        "4*x2 + 11*x3 <= 160",
        "14*x0 + 4*x2 <= 201",
        "11^2*x3 + 20^2*x4 <= 108",
        "18*x1 + 4*x2 + 20*x4 <= 322",
        "14*x0 + 18*x1 + 11*x3 <= 67",
        "14*x0 + 18*x1 + 4*x2 + 11*x3 + 20*x4 <= 67"
    ]
}
```