## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the number of vanilla protein bars
- $x_2$ represents the number of organic meal replacement shakes

## Step 2: Formulate the objective function
The objective is to minimize the cost. The cost of vanilla protein bars is $10 per serving and the cost of organic meal replacement shakes is $15 per serving. Therefore, the objective function can be written as:
Minimize $10x_1 + 15x_2$

## 3: Define the constraints
The constraints based on the problem description are:
- Protein requirement: $30x_1 + 10x_2 \geq 155$
- Carbs requirement: $50x_1 + 20x_2 \geq 140$
- Fat limit: $2x_1 + 5x_2 \leq 55$
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## 4: Symbolic representation of the problem
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'vanilla protein bars'), ('x2', 'organic meal replacement shakes')],
'objective_function': '10*x1 + 15*x2',
'constraints': [
'30*x1 + 10*x2 >= 155',
'50*x1 + 20*x2 >= 140',
'2*x1 + 5*x2 <= 55',
'x1 >= 0',
'x2 >= 0'
]
}
```

## 5: Gurobi code
Now, let's implement this problem using Gurobi in Python:
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="vanilla_protein_bars", lb=0)
    x2 = model.addVar(name="organic_meal_replacement_shakes", lb=0)

    # Define the objective function
    model.setObjective(10 * x1 + 15 * x2, gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(30 * x1 + 10 * x2 >= 155, name="protein_requirement")
    model.addConstr(50 * x1 + 20 * x2 >= 140, name="carbs_requirement")
    model.addConstr(2 * x1 + 5 * x2 <= 55, name="fat_limit")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Vanilla protein bars: {x1.varValue}")
        print(f"Organic meal replacement shakes: {x2.varValue}")
        print(f"Cost: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_julia_diet_problem()
```