Here's the formulation of the linear program and the Gurobi code to solve it:

**Decision Variables:**

* `x`: Number of sour drops
* `y`: Number of sour belts

**Objective Function:**

Minimize cost:  `0.5x + 0.4y`

**Constraints:**

* Sourness: `2x + 4y >= 30`
* Flavoring: `4x + 3y >= 40`
* Sour belt limit: `y <= 5`
* Non-negativity: `x >= 0`, `y >= 0`


```python
import gurobipy as gp
from gurobipy import GRB

try:
    # Create a new model
    m = gp.Model("candy_mixture")

    # Create variables
    x = m.addVar(vtype=GRB.CONTINUOUS, name="sour_drops")
    y = m.addVar(vtype=GRB.CONTINUOUS, name="sour_belts")


    # Set objective function
    m.setObjective(0.5*x + 0.4*y, GRB.MINIMIZE)

    # Add constraints
    m.addConstr(2*x + 4*y >= 30, "sourness")
    m.addConstr(4*x + 3*y >= 40, "flavoring")
    m.addConstr(y <= 5, "sour_belt_limit")

    # Optimize model
    m.optimize()

    # Print solution
    if m.status == GRB.OPTIMAL:
        print(f"Optimal Cost: {m.objVal}")
        print(f"Number of Sour Drops: {x.x}")
        print(f"Number of Sour Belts: {y.x}")
    elif m.status == GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print(f"Optimization ended with status {m.status}")


except gp.GurobiError as e:
    print(f"Error code {e.errno}: {e}")

except AttributeError:
    print('Encountered an attribute error')
```
