## Step 1: Define the symbolic representation of the problem
First, let's define the symbolic variables and the objective function based on the given problem description.

Let:
- $x_0$ represent 'milligrams of vitamin B6'
- $x_1$ represent 'milligrams of vitamin B2'
- $x_2$ represent 'milligrams of zinc'

The objective function to minimize is:
\[ 9x_0^2 + 1x_0x_2 + 2x_1^2 + 1x_1x_2 + 1x_2^2 + 6x_0 + 3x_1 + 1x_2 \]

## Step 2: List the constraints
The constraints given are:
1. $8.88x_0 \leq 157$
2. $12.48x_0 \leq 435$
3. $14.45x_1 \leq 157$
4. $10.75x_1 \leq 435$
5. $5.8x_2 \leq 157$
6. $8.29x_2 \leq 435$
7. $8.88x_0 + 14.45x_1 \geq 25$
8. $8.88x_0 + 5.8x_2 \geq 30$
9. $14.45x_1 + 5.8x_2 \geq 24$
10. $8.88x_0 + 14.45x_1 + 5.8x_2 \geq 24$
11. $12.48x_0 + 8.29x_2 \geq 88$
12. $10.75x_1^2 + 8.29x_2^2 \geq 89$
13. $12.48x_0 + 10.75x_1 + 8.29x_2 \geq 89$
14. $10x_0 - 5x_2 \geq 0$
15. $2x_1 - 3x_2 \geq 0$
16. $2x_0 - 5x_1 \geq 0$
17. $14.45x_1^2 + 5.8x_2^2 \leq 77$
18. $8.88x_0 + 14.45x_1 + 5.8x_2 \leq 134$
19. $12.48x_0 + 8.29x_2 \leq 357$
20. $x_0$ is continuous
21. $x_1$ is continuous
22. $x_2$ is integer

## Step 3: Convert the problem into Gurobi code
Now, let's convert this into Gurobi code.

```python
import gurobi

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

# Define the variables
x0 = model.addVar(name="x0", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, vtype=gurobi.GRB.CONTINUOUS)
x1 = model.addVar(name="x1", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, vtype=gurobi.GRB.CONTINUOUS)
x2 = model.addVar(name="x2", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, vtype=gurobi.GRB.INTEGER)

# Define the objective function
model.setObjective(9*x0**2 + x0*x2 + 2*x1**2 + x1*x2 + x2**2 + 6*x0 + 3*x1 + x2, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(8.88*x0 <= 157)
model.addConstr(12.48*x0 <= 435)
model.addConstr(14.45*x1 <= 157)
model.addConstr(10.75*x1 <= 435)
model.addConstr(5.8*x2 <= 157)
model.addConstr(8.29*x2 <= 435)
model.addConstr(8.88*x0 + 14.45*x1 >= 25)
model.addConstr(8.88*x0 + 5.8*x2 >= 30)
model.addConstr(14.45*x1 + 5.8*x2 >= 24)
model.addConstr(8.88*x0 + 14.45*x1 + 5.8*x2 >= 24)
model.addConstr(12.48*x0 + 8.29*x2 >= 88)
model.addConstr(10.75*x1**2 + 8.29*x2**2 >= 89)
model.addConstr(12.48*x0 + 10.75*x1 + 8.29*x2 >= 89)
model.addConstr(10*x0 - 5*x2 >= 0)
model.addConstr(2*x1 - 3*x2 >= 0)
model.addConstr(2*x0 - 5*x1 >= 0)
model.addConstr(14.45*x1**2 + 5.8*x2**2 <= 77)
model.addConstr(8.88*x0 + 14.45*x1 + 5.8*x2 <= 134)
model.addConstr(12.48*x0 + 8.29*x2 <= 357)

# 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)
else:
    print("The model is infeasible")
```

## Step 4: Symbolic representation
The symbolic representation of the problem is as follows:

```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B6'),
        ('x1', 'milligrams of vitamin B2'),
        ('x2', 'milligrams of zinc')
    ],
    'objective_function': '9*x0^2 + x0*x2 + 2*x1^2 + x1*x2 + x2^2 + 6*x0 + 3*x1 + x2',
    'constraints': [
        '8.88*x0 <= 157',
        '12.48*x0 <= 435',
        '14.45*x1 <= 157',
        '10.75*x1 <= 435',
        '5.8*x2 <= 157',
        '8.29*x2 <= 435',
        '8.88*x0 + 14.45*x1 >= 25',
        '8.88*x0 + 5.8*x2 >= 30',
        '14.45*x1 + 5.8*x2 >= 24',
        '8.88*x0 + 14.45*x1 + 5.8*x2 >= 24',
        '12.48*x0 + 8.29*x2 >= 88',
        '10.75*x1^2 + 8.29*x2^2 >= 89',
        '12.48*x0 + 10.75*x1 + 8.29*x2 >= 89',
        '10*x0 - 5*x2 >= 0',
        '2*x1 - 3*x2 >= 0',
        '2*x0 - 5*x1 >= 0',
        '14.45*x1^2 + 5.8*x2^2 <= 77',
        '8.88*x0 + 14.45*x1 + 5.8*x2 <= 134',
        '12.48*x0 + 8.29*x2 <= 357'
    ]
}
```