## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B1', 'milligrams of iron', 'grams of protein', 'milligrams of vitamin A']. Let's denote them as $x_1$, $x_2$, $x_3$, and $x_4$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ : milligrams of vitamin B1
- $x_2$ : milligrams of iron
- $x_3$ : grams of protein
- $x_4$ : milligrams of vitamin A

## 3: Define the objective function in symbolic notation
The objective function to maximize is: $1x_1 + 9x_2 + 9x_3 + 2x_4$.

## 4: List the constraints in symbolic notation
The constraints are:
- $1x_1 \leq 184$
- $9x_2 \leq 184$
- $5x_3 \leq 184$
- $3x_4 \leq 184$
- $9x_2 + 5x_3 \geq 33$
- $5x_3 + 3x_4 \leq 108$
- $1x_1 + 5x_3 \leq 181$
- $1x_1 + 9x_2 + 5x_3 \leq 158$
- $1x_1 + 5x_3 + 3x_4 \leq 177$
- $9x_2 + 5x_3 + 3x_4 \leq 123$
- $1x_1 + 9x_2 + 3x_4 \leq 141$
- $1x_1 + 9x_2 + 5x_3 + 3x_4 \leq 141$
- $x_1$ is a whole number
- $x_3$ is a whole number
- $x_4$ is a whole number

## 5: Write down the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B1'), 
        ('x2', 'milligrams of iron'), 
        ('x3', 'grams of protein'), 
        ('x4', 'milligrams of vitamin A')
    ], 
    'objective_function': '1*x1 + 9*x2 + 9*x3 + 2*x4', 
    'constraints': [
        '1*x1 <= 184', 
        '9*x2 <= 184', 
        '5*x3 <= 184', 
        '3*x4 <= 184', 
        '9*x2 + 5*x3 >= 33', 
        '5*x3 + 3*x4 <= 108', 
        '1*x1 + 5*x3 <= 181', 
        '1*x1 + 9*x2 + 5*x3 <= 158', 
        '1*x1 + 5*x3 + 3*x4 <= 177', 
        '9*x2 + 5*x3 + 3*x4 <= 123', 
        '1*x1 + 9*x2 + 3*x4 <= 141', 
        '1*x1 + 9*x2 + 5*x3 + 3*x4 <= 141'
    ]
}
```

## Step 6: Implement the optimization problem using Gurobi
```python
import gurobi

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

# Define the variables
x1 = m.addVar(name="x1", vtype=gurobi.GRB.INTEGER)  # milligrams of vitamin B1
x2 = m.addVar(name="x2")  # milligrams of iron
x3 = m.addVar(name="x3", vtype=gurobi.GRB.INTEGER)  # grams of protein
x4 = m.addVar(name="x4", vtype=gurobi.GRB.INTEGER)  # milligrams of vitamin A

# Objective function
m.setObjective(1*x1 + 9*x2 + 9*x3 + 2*x4, gurobi.GRB.MAXIMIZE)

# Constraints
m.addConstr(1*x1 <= 184)
m.addConstr(9*x2 <= 184)
m.addConstr(5*x3 <= 184)
m.addConstr(3*x4 <= 184)
m.addConstr(9*x2 + 5*x3 >= 33)
m.addConstr(5*x3 + 3*x4 <= 108)
m.addConstr(1*x1 + 5*x3 <= 181)
m.addConstr(1*x1 + 9*x2 + 5*x3 <= 158)
m.addConstr(1*x1 + 5*x3 + 3*x4 <= 177)
m.addConstr(9*x2 + 5*x3 + 3*x4 <= 123)
m.addConstr(1*x1 + 9*x2 + 3*x4 <= 141)
m.addConstr(1*x1 + 9*x2 + 5*x3 + 3*x4 <= 141)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of vitamin B1: {x1.varValue}")
    print(f"Milligrams of iron: {x2.varValue}")
    print(f"Grams of protein: {x3.varValue}")
    print(f"Milligrams of vitamin A: {x4.varValue}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```