## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. Let's define the symbolic variables:
- $x_0$ represents 'milligrams of iron'
- $x_1$ represents 'milligrams of vitamin B9'
- $x_2$ represents 'milligrams of vitamin B12'

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is: $1.1x_0^2 + 4.45x_1^2 + 7.36x_2^2 + 3.64x_0 + 2.76x_2$

## 3: List the constraints in symbolic notation
Constraints:
1. $13x_0 \leq 218$
2. $x_0 \leq 177 / 1 = 177$ is incorrect, it is $1x_0 \leq 177$
3. $23x_0 \leq 289$
4. $4x_1 \leq 218$
5. $12x_1 \leq 177$
6. $10x_1 \leq 289$
7. $16x_2 \leq 218$
8. $21x_2 \leq 177$
9. $14x_2 \leq 289$
10. $4x_1 + 16x_2 \geq 26$
11. $13x_0 + 16x_2 \geq 24$
12. $1x_0 + 21x_2 \geq 43$
13. $12^2x_1^2 + 21^2x_2^2 \geq 35$ is incorrect, it is $12x_1 + 21x_2 \geq 35$ or $144x_1^2 + 441x_2^2 \geq 35$
14. $10x_1 + 14x_2 \geq 78$
15. $23x_0 + 14x_2 \geq 37$
16. $13x_0 + 4x_1 \leq 126$
17. $13x_0 + 16x_2 \leq 110$
18. $13x_0 + 4x_1 + 16x_2 \leq 110$
19. $12x_1 + 21x_2 \leq 157$
20. $1x_0 + 12x_1 \leq 157$
21. $1x_0 + 12x_1 + 21x_2 \leq 157$
22. $100x_1^2 + 196x_2^2 \leq 231$ is incorrect it is $10x_1 + 14x_2 \leq \sqrt{231}$ or simply $100x_1^2 + 196x_2^2 \leq 231$
23. $529x_0^2 + 196x_2^2 \leq 105$ 
24. $23x_0 + 10x_1 + 14x_2 \leq 105$

## 4: Create a symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of iron'), 
        ('x1', 'milligrams of vitamin B9'), 
        ('x2', 'milligrams of vitamin B12')
    ], 
    'objective_function': '1.1*x0^2 + 4.45*x1^2 + 7.36*x2^2 + 3.64*x0 + 2.76*x2', 
    'constraints': [
        '13*x0 <= 218',
        '1*x0 <= 177',
        '23*x0 <= 289',
        '4*x1 <= 218',
        '12*x1 <= 177',
        '10*x1 <= 289',
        '16*x2 <= 218',
        '21*x2 <= 177',
        '14*x2 <= 289',
        '4*x1 + 16*x2 >= 26',
        '13*x0 + 16*x2 >= 24',
        '1*x0 + 21*x2 >= 43',
        '144*x1^2 + 441*x2^2 >= 35',
        '10*x1 + 14*x2 >= 78',
        '23*x0 + 14*x2 >= 37',
        '13*x0 + 4*x1 <= 126',
        '13*x0 + 16*x2 <= 110',
        '13*x0 + 4*x1 + 16*x2 <= 110',
        '12*x1 + 21*x2 <= 157',
        '1*x0 + 12*x1 <= 157',
        '1*x0 + 12*x1 + 21*x2 <= 157',
        '100*x1^2 + 196*x2^2 <= 231',
        '529*x0^2 + 196*x2^2 <= 105',
        '23*x0 + 10*x1 + 14*x2 <= 105'
    ]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

def optimize_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name="milligrams of iron", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x1 = model.addVar(name="milligrams of vitamin B9", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="milligrams of vitamin B12", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

    # Objective function
    model.setObjective(1.1*x0**2 + 4.45*x1**2 + 7.36*x2**2 + 3.64*x0 + 2.76*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(13*x0 <= 218)
    model.addConstr(x0 <= 177)
    model.addConstr(23*x0 <= 289)
    model.addConstr(4*x1 <= 218)
    model.addConstr(12*x1 <= 177)
    model.addConstr(10*x1 <= 289)
    model.addConstr(16*x2 <= 218)
    model.addConstr(21*x2 <= 177)
    model.addConstr(14*x2 <= 289)
    model.addConstr(4*x1 + 16*x2 >= 26)
    model.addConstr(13*x0 + 16*x2 >= 24)
    model.addConstr(x0 + 21*x2 >= 43)
    model.addConstr(144*x1**2 + 441*x2**2 >= 35)
    model.addConstr(10*x1 + 14*x2 >= 78)
    model.addConstr(23*x0 + 14*x2 >= 37)
    model.addConstr(13*x0 + 4*x1 <= 126)
    model.addConstr(13*x0 + 16*x2 <= 110)
    model.addConstr(13*x0 + 4*x1 + 16*x2 <= 110)
    model.addConstr(12*x1 + 21*x2 <= 157)
    model.addConstr(x0 + 12*x1 <= 157)
    model.addConstr(x0 + 12*x1 + 21*x2 <= 157)
    model.addConstr(100*x1**2 + 196*x2**2 <= 231)
    model.addConstr(529*x0**2 + 196*x2**2 <= 105)
    model.addConstr(23*x0 + 10*x1 + 14*x2 <= 105)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of iron: {x0.varValue}")
        print(f"Milligrams of vitamin B9: {x1.varValue}")
        print(f"Milligrams of vitamin B12: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```