## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin A', 'milligrams of iron', and 'milligrams of vitamin K'. Let's denote them as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : 'milligrams of vitamin A'
- $x_1$ : 'milligrams of iron'
- $x_2$ : 'milligrams of vitamin K'

## 3: Define the objective function in symbolic notation
The objective function to minimize is $6.04x_0 + 2.29x_1 + 8.63x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $2x_0 \leq 249$
- $9x_0 \leq 208$
- $13x_1 \leq 249$
- $11x_1 \leq 208$
- $14x_2 \leq 249$
- $5x_2 \leq 208$
- $2x_0 + 14x_2 \geq 50$
- $2x_0 + 13x_1 + 14x_2 \geq 50$
- $11x_1 + 5x_2 \geq 43$
- $9x_0 + 11x_1 \geq 24$
- $9x_0 + 11x_1 + 5x_2 \geq 24$
- $x_1 - 10x_2 \geq 0$
- $2x_0 + 13x_1 \leq 105$
- $2x_0 + 14x_2 \leq 234$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin A'), 
        ('x1', 'milligrams of iron'), 
        ('x2', 'milligrams of vitamin K')
    ], 
    'objective_function': '6.04*x0 + 2.29*x1 + 8.63*x2', 
    'constraints': [
        '2*x0 <= 249', 
        '9*x0 <= 208', 
        '13*x1 <= 249', 
        '11*x1 <= 208', 
        '14*x2 <= 249', 
        '5*x2 <= 208', 
        '2*x0 + 14*x2 >= 50', 
        '2*x0 + 13*x1 + 14*x2 >= 50', 
        '11*x1 + 5*x2 >= 43', 
        '9*x0 + 11*x1 >= 24', 
        '9*x0 + 11*x1 + 5*x2 >= 24', 
        'x1 - 10*x2 >= 0', 
        '2*x0 + 13*x1 <= 105', 
        '2*x0 + 14*x2 <= 234'
    ]
}
```

## Step 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

def solve_optimization_problem():
    # Create a new Gurobi model
    model = gurobi.Model()

    # Define the variables
    x0 = model.addVar(name="milligrams_of_vitamin_A", lb=-float('inf'))
    x1 = model.addVar(name="milligrams_of_iron", lb=-float('inf'))
    x2 = model.addVar(name="milligrams_of_vitamin_K", lb=-float('inf'))

    # Define the objective function
    model.setObjective(6.04 * x0 + 2.29 * x1 + 8.63 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(2 * x0 <= 249)
    model.addConstr(9 * x0 <= 208)
    model.addConstr(13 * x1 <= 249)
    model.addConstr(11 * x1 <= 208)
    model.addConstr(14 * x2 <= 249)
    model.addConstr(5 * x2 <= 208)
    model.addConstr(2 * x0 + 14 * x2 >= 50)
    model.addConstr(2 * x0 + 13 * x1 + 14 * x2 >= 50)
    model.addConstr(11 * x1 + 5 * x2 >= 43)
    model.addConstr(9 * x0 + 11 * x1 >= 24)
    model.addConstr(9 * x0 + 11 * x1 + 5 * x2 >= 24)
    model.addConstr(x1 - 10 * x2 >= 0)
    model.addConstr(2 * x0 + 13 * x1 <= 105)
    model.addConstr(2 * x0 + 14 * x2 <= 234)

    # Optimize the model
    model.optimize()

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

solve_optimization_problem()
```