## Step 1: Define the symbolic representation of the problem
Let's denote the number of bags of Drummondville blend as $x_1$ and the number of bags of Victoriaville blend as $x_2$. The profit from each bag of Drummondville sold is $5, and from each bag of Victoriaville sold is $7. The objective is to maximize the total profit $P = 5x_1 + 7x_2$.

## Step 2: Identify the constraints
Each bag of Drummondville blend contains 600 grams of arabica beans and 400 grams of robusta beans. Each bag of Victoriaville blend contains 375 grams of arabica beans and 625 grams of robusta beans. The total production must not exceed 24000 grams of arabica beans and 17000 grams of robusta beans. This gives us two constraints:
1. $600x_1 + 375x_2 \leq 24000$ (arabica beans constraint)
2. $400x_1 + 625x_2 \leq 17000$ (robusta beans constraint)
Additionally, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of bags cannot be negative.

## 3: Symbolic representation in JSON format
```json
{
'sym_variables': [('x1', 'Drummondville blend bags'), ('x2', 'Victoriaville blend bags')],
'objective_function': '5*x1 + 7*x2',
'constraints': [
    '600*x1 + 375*x2 <= 24000',
    '400*x1 + 625*x2 <= 17000',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 4: Convert the problem into Gurobi code in Python
```python
import gurobipy as gp

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

# Define the variables
x1 = model.addVar(name="Drummondville_blend_bags", lb=0, vtype=gp.GRB.CONTINUOUS)
x2 = model.addVar(name="Victoriaville_blend_bags", lb=0, vtype=gp.GRB.CONTINUOUS)

# Define the objective function
model.setObjective(5*x1 + 7*x2, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(600*x1 + 375*x2 <= 24000, name="Arabica_beans_constraint")
model.addConstr(400*x1 + 625*x2 <= 17000, name="Robusta_beans_constraint")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Number of Drummondville blend bags: {x1.varValue}")
    print(f"Number of Victoriaville blend bags: {x2.varValue}")
    print(f"Maximal profit: {model.objVal}")
else:
    print("No optimal solution found.")
```