## Step 1: Define the optimization problem
The problem is to maximize the objective function: $1.1x_0^2 + 4.45x_1^2 + 7.36x_2^2 + 3.64x_0 + 2.76x_2$, where $x_0$ represents the milligrams of iron, $x_1$ represents the milligrams of vitamin B9, and $x_2$ represents the milligrams of vitamin B12.

## Step 2: List all the constraints
The constraints are as follows:
- $13x_0 \leq 218$
- $x_0 \leq 0$ is not given, but $x_0 \geq 0$ is implied for practical purposes, however, we only have upper bounds.
- $1x_0 \leq 177$
- $23x_0 \leq 289$
- $4x_1 \leq 218$
- $12x_1 \leq 177$
- $10x_1 \leq 289$
- $16x_2 \leq 218$
- $21x_2 \leq 177$
- $14x_2 \leq 289$
- $4x_1 + 16x_2 \geq 26$
- $13x_0 + 16x_2 \geq 24$
- $1x_0 + 21x_2 \geq 43$
- $12^2x_1^2 + 21^2x_2^2 \geq 35$
- $10x_1 + 14x_2 \geq 78$
- $23x_0 + 14x_2 \geq 37$
- $13x_0 + 4x_1 \leq 126$
- $13x_0 + 16x_2 \leq 110$
- $13x_0 + 4x_1 + 16x_2 \leq 110$
- $12x_1 + 21x_2 \leq 157$
- $1x_0 + 12x_1 \leq 157$
- $1x_0 + 12x_1 + 21x_2 \leq 157$
- $10^2x_1^2 + 14^2x_2^2 \leq 231$
- $23^2x_0^2 + 14^2x_2^2 \leq 105$
- $23x_0 + 10x_1 + 14x_2 \leq 105$

## 3: Convert the problem into Gurobi code
We will use Gurobi's Python API to model and solve this problem.

```python
import gurobi as gp

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

# Define the variables
x0 = m.addVar(name="milligrams_of_iron", lb=0)  # Assuming non-negative for practical purposes
x1 = m.addVar(name="milligrams_of_vitamin_B9", lb=0)  # Assuming non-negative for practical purposes
x2 = m.addVar(name="milligrams_of_vitamin_B12", lb=0)  # Assuming non-negative for practical purposes

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

# Constraints
m.addConstr(13*x0 <= 218)
m.addConstr(x0 <= 177)
m.addConstr(23*x0 <= 289)

m.addConstr(4*x1 <= 218)
m.addConstr(12*x1 <= 177)
m.addConstr(10*x1 <= 289)

m.addConstr(16*x2 <= 218)
m.addConstr(21*x2 <= 177)
m.addConstr(14*x2 <= 289)

m.addConstr(4*x1 + 16*x2 >= 26)
m.addConstr(13*x0 + 16*x2 >= 24)
m.addConstr(x0 + 21*x2 >= 43)
m.addConstr(12**2*x1**2 + 21**2*x2**2 >= 35)
m.addConstr(10*x1 + 14*x2 >= 78)
m.addConstr(23*x0 + 14*x2 >= 37)

m.addConstr(13*x0 + 4*x1 <= 126)
m.addConstr(13*x0 + 16*x2 <= 110)
m.addConstr(13*x0 + 4*x1 + 16*x2 <= 110)

m.addConstr(12*x1 + 21*x2 <= 157)
m.addConstr(x0 + 12*x1 <= 157)
m.addConstr(x0 + 12*x1 + 21*x2 <= 157)

m.addConstr(10**2*x1**2 + 14**2*x2**2 <= 231)
m.addConstr(23**2*x0**2 + 14**2*x2**2 <= 105)
m.addConstr(23*x0 + 10*x1 + 14*x2 <= 105)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.Status.OPTIMAL:
    print("Optimal Solution:")
    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: {m.objVal}")
else:
    print("No optimal solution found.")
```