## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of zinc', 'milligrams of calcium', and 'milligrams of vitamin B4', which can be represented symbolically 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 zinc
- $x_1$ : milligrams of calcium
- $x_2$ : milligrams of vitamin B4

## 3: Define the objective function in symbolic notation
The objective function to minimize is $8.54x_0 + 6.37x_1 + 8.42x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $17x_0 \leq 282$
- $23x_0 \leq 170$
- $5x_1 \leq 282$
- $27x_1 \leq 170$
- $18x_2 \leq 282$
- $1x_2 \leq 170$
- $5x_1 + 18x_2 \geq 64$
- $17x_0 + 5x_1 \geq 58$
- $17x_0 + 5x_1 + 18x_2 \geq 58$
- $27x_1 + 1x_2 \geq 37$
- $23x_0 + 27x_1 \geq 45$
- $23x_0 + 1x_2 \geq 34$
- $23x_0 + 27x_1 + 1x_2 \geq 34$
- $3x_0 - 5x_1 \geq 0$
- $4x_0 - 4x_2 \geq 0$
- $17x_0 + 18x_2 \leq 149$
- $17x_0 + 5x_1 + 18x_2 \leq 249$
- $x_0$ is continuous
- $x_1$ is integer
- $x_2$ is continuous

## 5: Create the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of zinc'), 
        ('x1', 'milligrams of calcium'), 
        ('x2', 'milligrams of vitamin B4')
    ], 
    'objective_function': '8.54*x0 + 6.37*x1 + 8.42*x2', 
    'constraints': [
        '17*x0 <= 282',
        '23*x0 <= 170',
        '5*x1 <= 282',
        '27*x1 <= 170',
        '18*x2 <= 282',
        '1*x2 <= 170',
        '5*x1 + 18*x2 >= 64',
        '17*x0 + 5*x1 >= 58',
        '17*x0 + 5*x1 + 18*x2 >= 58',
        '27*x1 + 1*x2 >= 37',
        '23*x0 + 27*x1 >= 45',
        '23*x0 + 1*x2 >= 34',
        '23*x0 + 27*x1 + 1*x2 >= 34',
        '3*x0 - 5*x1 >= 0',
        '4*x0 - 4*x2 >= 0',
        '17*x0 + 18*x2 <= 149',
        '17*x0 + 5*x1 + 18*x2 <= 249'
    ]
}
```

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

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

    # Define variables
    x0 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="milligrams_of_zinc", vtype=gurobi.GRB.CONTINUOUS)
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="milligrams_of_calcium", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="milligrams_of_vitamin_B4", vtype=gurobi.GRB.CONTINUOUS)

    # Objective function
    model.setObjective(8.54 * x0 + 6.37 * x1 + 8.42 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(17 * x0 <= 282)
    model.addConstr(23 * x0 <= 170)
    model.addConstr(5 * x1 <= 282)
    model.addConstr(27 * x1 <= 170)
    model.addConstr(18 * x2 <= 282)
    model.addConstr(1 * x2 <= 170)
    model.addConstr(5 * x1 + 18 * x2 >= 64)
    model.addConstr(17 * x0 + 5 * x1 >= 58)
    model.addConstr(17 * x0 + 5 * x1 + 18 * x2 >= 58)
    model.addConstr(27 * x1 + 1 * x2 >= 37)
    model.addConstr(23 * x0 + 27 * x1 >= 45)
    model.addConstr(23 * x0 + 1 * x2 >= 34)
    model.addConstr(23 * x0 + 27 * x1 + 1 * x2 >= 34)
    model.addConstr(3 * x0 - 5 * x1 >= 0)
    model.addConstr(4 * x0 - 4 * x2 >= 0)
    model.addConstr(17 * x0 + 18 * x2 <= 149)
    model.addConstr(17 * x0 + 5 * x1 + 18 * x2 <= 249)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of zinc: {x0.varValue}")
        print(f"Milligrams of calcium: {x1.varValue}")
        print(f"Milligrams of vitamin B4: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```