## Step 1: Define the symbolic representation of the problem
Let's denote the number of packages of Vanilla flavor as $x_1$ and the number of packages of Mocha flavor as $x_2$. The objective is to minimize the cost, which can be represented as $2x_1 + 3x_2$. The constraints are:
- $2x_1 + 3x_2 \geq 60$ (at least 60 units of caffeine)
- $2x_1 + 5x_2 \geq 50$ (at least 50 units of sugar)
- $x_1, x_2 \geq 0$ (non-negativity constraint, as we cannot buy a negative number of packages)

## Step 2: Convert the problem into a symbolic representation
The symbolic representation can be described as:
```json
{
'sym_variables': [('x1', 'Vanilla'), ('x2', 'Mocha')],
'objective_function': '2*x1 + 3*x2',
'constraints': ['2*x1 + 3*x2 >= 60', '2*x1 + 5*x2 >= 50', 'x1 >= 0', 'x2 >= 0']
}
```

## Step 3: Implement the problem in Gurobi code
To solve this linear programming problem using Gurobi, we will use the Gurobi Python interface. First, ensure you have Gurobi installed in your Python environment. You can install it via pip: `pip install gurobi`.

```python
import gurobi as gp

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

# Define the variables
x1 = model.addVar(name="Vanilla", lb=0, vtype=gp.GRB.INTEGER)  # Number of Vanilla packages
x2 = model.addVar(name="Mocha", lb=0, vtype=gp.GRB.INTEGER)   # Number of Mocha packages

# Define the objective function
model.setObjective(2*x1 + 3*x2, gp.GRB.MINIMIZE)

# Define the constraints
model.addConstr(2*x1 + 3*x2 >= 60, name="Caffeine_Constraint")  # Caffeine constraint
model.addConstr(2*x1 + 5*x2 >= 50, name="Sugar_Constraint")    # Sugar constraint

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
    print(f"Minimum cost: ${model.objVal:.2f}")
else:
    print("No optimal solution found")
```