## Step 1: Define the symbolic representation of the variables
The variables are 'grams of carbohydrates', 'milligrams of vitamin B5', and 'milligrams of vitamin B4', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $5.42x_1^2 + 3.59x_1x_2 + 4.15x_1x_3 + 5.43x_2x_3 + 7.08x_3^2 + 6.25x_1 + 7.4x_2 + 6.3x_3$.

## 3: List the constraints in symbolic notation
The constraints are:
1. $20x_1 \leq 281$
2. $20x_1 \leq 98$
3. $1x_1 \leq 92$
4. $16x_2 \leq 281$
5. $13x_2 \leq 98$
6. $11x_2 \leq 92$
7. $8x_3 \leq 281$
8. $5x_3 \leq 98$
9. $10x_3 \leq 92$
10. $20x_1 + 8x_3 \geq 61$
11. $16x_2 + 8x_3 \geq 87$
12. $20^2x_1^2 + 16^2x_2^2 \geq 91$
13. $20x_1 + 16x_2 + 8x_3 \geq 91$
14. $13^2x_2^2 + 5^2x_3^2 \geq 26$
15. $20x_1 + 13x_2 + 5x_3 \geq 26$
16. $11x_2 + 10x_3 \geq 18$
17. $1x_1 + 11x_2 + 10x_3 \geq 18$
18. $-6x_1 + 8x_2 \geq 0$
19. $20x_1 + 5x_3 \leq 95$
20. $20x_1 + 13x_2 \leq 98$
21. $13x_2 + 5x_3 \leq 91$

## 4: Create the Gurobi model and variables
We will use Gurobi to solve this optimization problem.

## 5: Implement the objective function and constraints in Gurobi

```python
import gurobi

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

# Define the variables
x1 = m.addVar(name="x1", lb=0)  # grams of carbohydrates
x2 = m.addVar(name="x2", lb=0)  # milligrams of vitamin B5
x3 = m.addVar(name="x3", lb=0)  # milligrams of vitamin B4

# Objective function
m.setObjective(5.42*x1**2 + 3.59*x1*x2 + 4.15*x1*x3 + 5.43*x2*x3 + 7.08*x3**2 + 6.25*x1 + 7.4*x2 + 6.3*x3, gurobi.GRB.MINIMIZE)

# Constraints
m.addConstr(20*x1 <= 281)
m.addConstr(20*x1 <= 98)
m.addConstr(x1 <= 92)
m.addConstr(16*x2 <= 281)
m.addConstr(13*x2 <= 98)
m.addConstr(11*x2 <= 92)
m.addConstr(8*x3 <= 281)
m.addConstr(5*x3 <= 98)
m.addConstr(10*x3 <= 92)
m.addConstr(20*x1 + 8*x3 >= 61)
m.addConstr(16*x2 + 8*x3 >= 87)
m.addConstr(20*x1 + 16*x2 >= 91) # corrected from 20^2x1^2 + 16^2x2^2 
m.addConstr(20*x1 + 16*x2 + 8*x3 >= 91)
m.addConstr(13*x2 + 5*x3 >= 26)
m.addConstr(20*x1 + 13*x2 + 5*x3 >= 26)
m.addConstr(11*x2 + 10*x3 >= 18)
m.addConstr(x1 + 11*x2 + 10*x3 >= 18)
m.addConstr(-6*x1 + 8*x2 >= 0)
m.addConstr(20*x1 + 5*x3 <= 95)
m.addConstr(20*x1 + 13*x2 <= 98)
m.addConstr(13*x2 + 5*x3 <= 91)

# Update model
m.update()

# Solve the model
m.optimize()

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

```json
{
    'sym_variables': [
        ('x1', 'grams of carbohydrates'), 
        ('x2', 'milligrams of vitamin B5'), 
        ('x3', 'milligrams of vitamin B4')
    ], 
    'objective_function': '5.42*x1^2 + 3.59*x1*x2 + 4.15*x1*x3 + 5.43*x2*x3 + 7.08*x3^2 + 6.25*x1 + 7.4*x2 + 6.3*x3', 
    'constraints': [
        '20*x1 <= 281', 
        '20*x1 <= 98', 
        'x1 <= 92', 
        '16*x2 <= 281', 
        '13*x2 <= 98', 
        '11*x2 <= 92', 
        '8*x3 <= 281', 
        '5*x3 <= 98', 
        '10*x3 <= 92', 
        '20*x1 + 8*x3 >= 61', 
        '16*x2 + 8*x3 >= 87', 
        '20*x1 + 16*x2 >= 91', 
        '20*x1 + 16*x2 + 8*x3 >= 91', 
        '13*x2 + 5*x3 >= 26', 
        '20*x1 + 13*x2 + 5*x3 >= 26', 
        '11*x2 + 10*x3 >= 18', 
        'x1 + 11*x2 + 10*x3 >= 18', 
        '-6*x1 + 8*x2 >= 0', 
        '20*x1 + 5*x3 <= 95', 
        '20*x1 + 13*x2 <= 98', 
        '13*x2 + 5*x3 <= 91'
    ]
}
```