```json
{
  "sym_variables": [
    ("x1", "chewable pills"),
    ("x2", "regular pills")
  ],
  "objective_function": "0.5 * x1 + 0.4 * x2",
  "constraints": [
    "2 * x1 + 3 * x2 >= 30",  // Vitamin A
    "3 * x1 + 2 * x2 >= 20",  // Vitamin C
    "3 * x1 + 4 * x2 >= 40",  // Vitamin D
    "2 * x1 + 4 * x2 >= 30",  // Vitamin E
    "x1 >= 0",
    "x2 >= 0"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
chewable = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="chewable")
regular = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="regular")


# Set objective function
model.setObjective(0.5 * chewable + 0.4 * regular, gp.GRB.MINIMIZE)

# Add constraints
model.addConstr(2 * chewable + 3 * regular >= 30, "VitaminA")
model.addConstr(3 * chewable + 2 * regular >= 20, "VitaminC")
model.addConstr(3 * chewable + 4 * regular >= 40, "VitaminD")
model.addConstr(2 * chewable + 4 * regular >= 30, "VitaminE")


# Optimize model
model.optimize()

# Print results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal cost: ${model.objVal:.2f}")
    print(f"Chewable pills: {chewable.x:.2f}")
    print(f"Regular pills: {regular.x:.2f}")
elif model.status == gp.GRB.INFEASIBLE:
    print("The problem is infeasible.")
else:
    print(f"Optimization ended with status {model.status}")

```