## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_0$ represents 'milligrams of vitamin B12'
- $x_1$ represents 'grams of fiber'
- $x_2$ represents 'milligrams of vitamin A'
- $x_3$ represents 'milligrams of vitamin D'

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is: 
$8.54x_0^2 + 9.47x_0x_1 + 5.05x_0x_2 + 5.04x_1^2 + 6.27x_1x_2 + 1.01x_1x_3 + 9.4x_2x_3 + 7.08x_3^2 + 2.74x_1 + 2.72x_2 + 1.13x_3$

## 3: List the constraints in symbolic notation
Constraints:
1. $9x_0 \leq 240$
2. $9x_0 \leq 237$
3. $7x_1 \leq 240$
4. $13x_1 \leq 237$
5. $5x_2 \leq 240$
6. $9x_2 \leq 237$
7. $2x_3 \leq 240$
8. $14x_3 \leq 237$
9. $5x_2^2 + 2x_3^2 \geq 54$
10. $9x_0^2 + 2x_3^2 \geq 47$
11. $7x_1 + 2x_3 \geq 57$
12. $9x_0^2 + 5x_2^2 + 2x_3^2 \geq 45$
13. $7x_1^2 + 5x_2^2 + 2x_3^2 \geq 45$
14. $9x_0 + 7x_1 + 5x_2 \geq 45$
15. $9x_0 + 5x_2 + 2x_3 \geq 53$
16. $7x_1 + 5x_2 + 2x_3 \geq 53$
17. $9x_0 + 7x_1 + 5x_2 \geq 53$
18. $9x_0 + 5x_2 + 2x_3 \geq 35$
19. $7x_1^2 + 5x_2^2 + 2x_3^2 \geq 35$
20. $9x_0 + 7x_1 + 5x_2 \geq 35$
21. $9x_0 + 7x_1 + 5x_2 + 2x_3 \geq 35$
22. $13x_1^2 + 9x_2^2 \geq 26$
23. $13x_1 + 14x_3 \geq 47$
24. $9x_2 + 14x_3 \geq 51$
25. $9x_0^2 + 14x_3^2 \geq 19$
26. $9x_0 + 7x_1 + 5x_2 + 14x_3 \geq 19$
27. $2x_0 - 10x_3 \geq 0$
28. $-9x_0 + 6x_1 \geq 0$
29. $7x_1 + 5x_2 \leq 106$
30. $9x_0 + 5x_2 \leq 201$
31. $9x_0 + 7x_1 \leq 122$
32. $9x_0 + 2x_3 \leq 193$
33. $9x_0^2 + 5x_2^2 + 2x_3^2 \leq 156$
34. $9x_0 + 7x_1 + 5x_2 \leq 171$
35. $13x_1^2 + 2x_3^2 \leq 221$

## 4: Create the Gurobi model and variables
We will now create a Gurobi model and define the variables.

```python
import gurobi

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

# Define the variables
x0 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name='x0')
x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name='x1')
x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name='x2')
x3 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name='x3')

# Objective function
model.setObjective(8.54*x0**2 + 9.47*x0*x1 + 5.05*x0*x2 + 5.04*x1**2 + 6.27*x1*x2 + 1.01*x1*x3 + 9.4*x2*x3 + 7.08*x3**2 + 2.74*x1 + 2.72*x2 + 1.13*x3, gurobi.GRB.MINIMIZE)

# Constraints
model.addConstr(9*x0 <= 240)
model.addConstr(9*x0 <= 237)
model.addConstr(7*x1 <= 240)
model.addConstr(13*x1 <= 237)
model.addConstr(5*x2 <= 240)
model.addConstr(9*x2 <= 237)
model.addConstr(2*x3 <= 240)
model.addConstr(14*x3 <= 237)
model.addConstr(5*x2**2 + 2*x3**2 >= 54)
model.addConstr(9*x0**2 + 2*x3**2 >= 47)
model.addConstr(7*x1 + 2*x3 >= 57)
model.addConstr(9*x0**2 + 5*x2**2 + 2*x3**2 >= 45)
model.addConstr(7*x1**2 + 5*x2**2 + 2*x3**2 >= 45)
model.addConstr(9*x0 + 7*x1 + 5*x2 >= 45)
model.addConstr(9*x0 + 5*x2 + 2*x3 >= 53)
model.addConstr(7*x1 + 5*x2 + 2*x3 >= 53)
model.addConstr(9*x0 + 7*x1 + 5*x2 >= 53)
model.addConstr(9*x0 + 5*x2 + 2*x3 >= 35)
model.addConstr(7*x1**2 + 5*x2**2 + 2*x3**2 >= 35)
model.addConstr(9*x0 + 7*x1 + 5*x2 >= 35)
model.addConstr(9*x0 + 7*x1 + 5*x2 + 2*x3 >= 35)
model.addConstr(13*x1**2 + 9*x2**2 >= 26)
model.addConstr(13*x1 + 14*x3 >= 47)
model.addConstr(9*x2 + 14*x3 >= 51)
model.addConstr(9*x0**2 + 14*x3**2 >= 19)
model.addConstr(9*x0 + 7*x1 + 5*x2 + 14*x3 >= 19)
model.addConstr(2*x0 - 10*x3 >= 0)
model.addConstr(-9*x0 + 6*x1 >= 0)
model.addConstr(7*x1 + 5*x2 <= 106)
model.addConstr(9*x0 + 5*x2 <= 201)
model.addConstr(9*x0 + 7*x1 <= 122)
model.addConstr(9*x0 + 2*x3 <= 193)
model.addConstr(9*x0**2 + 5*x2**2 + 2*x3**2 <= 156)
model.addConstr(9*x0 + 7*x1 + 5*x2 <= 171)
model.addConstr(13*x1**2 + 2*x3**2 <= 221)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objVal)
    print('x0: ', x0.varValue)
    print('x1: ', x1.varValue)
    print('x2: ', x2.varValue)
    print('x3: ', x3.varValue)
else:
    print('No solution found')
```

## 5: Symbolic representation in JSON format
```json
{
    'sym_variables': [
        ['x0', 'milligrams of vitamin B12'],
        ['x1', 'grams of fiber'],
        ['x2', 'milligrams of vitamin A'],
        ['x3', 'milligrams of vitamin D']
    ],
    'objective_function': '8.54*x0^2 + 9.47*x0*x1 + 5.05*x0*x2 + 5.04*x1^2 + 6.27*x1*x2 + 1.01*x1*x3 + 9.4*x2*x3 + 7.08*x3^2 + 2.74*x1 + 2.72*x2 + 1.13*x3',
    'constraints': [
        '9*x0 <= 240',
        '9*x0 <= 237',
        '7*x1 <= 240',
        '13*x1 <= 237',
        '5*x2 <= 240',
        '9*x2 <= 237',
        '2*x3 <= 240',
        '14*x3 <= 237',
        '5*x2^2 + 2*x3^2 >= 54',
        '9*x0^2 + 2*x3^2 >= 47',
        '7*x1 + 2*x3 >= 57',
        '9*x0^2 + 5*x2^2 + 2*x3^2 >= 45',
        '7*x1^2 + 5*x2^2 + 2*x3^2 >= 45',
        '9*x0 + 7*x1 + 5*x2 >= 45',
        '9*x0 + 5*x2 + 2*x3 >= 53',
        '7*x1 + 5*x2 + 2*x3 >= 53',
        '9*x0 + 7*x1 + 5*x2 >= 53',
        '9*x0 + 5*x2 + 2*x3 >= 35',
        '7*x1^2 + 5*x2^2 + 2*x3^2 >= 35',
        '9*x0 + 7*x1 + 5*x2 >= 35',
        '9*x0 + 7*x1 + 5*x2 + 2*x3 >= 35',
        '13*x1^2 + 9*x2^2 >= 26',
        '13*x1 + 14*x3 >= 47',
        '9*x2 + 14*x3 >= 51',
        '9*x0^2 + 14*x3^2 >= 19',
        '9*x0 + 7*x1 + 5*x2 + 14*x3 >= 19',
        '2*x0 - 10*x3 >= 0',
        '-9*x0 + 6*x1 >= 0',
        '7*x1 + 5*x2 <= 106',
        '9*x0 + 5*x2 <= 201',
        '9*x0 + 7*x1 <= 122',
        '9*x0 + 2*x3 <= 193',
        '9*x0^2 + 5*x2^2 + 2*x3^2 <= 156',
        '9*x0 + 7*x1 + 5*x2 <= 171',
        '13*x1^2 + 2*x3^2 <= 221'
    ]
}
```