## Step 1: Define the symbolic representation of the problem

Let's define the symbolic variables:
- $x_1$ represents the number of chewable pills
- $x_2$ represents the number of regular pills

The objective is to minimize the cost, which is $0.50x_1 + 0.40x_2$.

## Step 2: Define the constraints based on the vitamin requirements

The constraints are as follows:
- Vitamin A: $2x_1 + 3x_2 \geq 30$
- Vitamin C: $3x_1 + 2x_2 \geq 20$
- Vitamin D: $3x_1 + 4x_2 \geq 40$
- Vitamin E: $2x_1 + 4x_2 \geq 30$
Also, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of pills cannot be negative.

## 3: Express the problem in the required symbolic format

```json
{
    'sym_variables': [('x1', 'chewable pills'), ('x2', 'regular pills')],
    'objective_function': '0.50*x1 + 0.40*x2',
    'constraints': [
        '2*x1 + 3*x2 >= 30',
        '3*x1 + 2*x2 >= 20',
        '3*x1 + 4*x2 >= 40',
        '2*x1 + 4*x2 >= 30',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 4: Convert the problem into Gurobi code

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Vitamin_Supplement")

# Define the variables
x1 = model.addVar(name="chewable_pills", lb=0, vtype=gp.GRB.INTEGER)
x2 = model.addVar(name="regular_pills", lb=0, vtype=gp.GRB.INTEGER)

# Define the objective function
model.setObjective(0.50*x1 + 0.40*x2, gp.GRB.MINIMIZE)

# Add the constraints
model.addConstr(2*x1 + 3*x2 >= 30, name="vitamin_A")
model.addConstr(3*x1 + 2*x2 >= 20, name="vitamin_C")
model.addConstr(3*x1 + 4*x2 >= 40, name="vitamin_D")
model.addConstr(2*x1 + 4*x2 >= 30, name="vitamin_E")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Number of chewable pills: {x1.varValue}")
    print(f"Number of regular pills: {x2.varValue}")
    print(f"Total cost: {model.objVal}")
else:
    print("No optimal solution found.")
```