## Symbolic Representation

To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation.

Let's define the variables:
- $x_1$ = Number of chocolate croissants
- $x_2$ = Number of strawberry croissants

The objective is to maximize the total monthly profit. The profit per chocolate croissant is $4, and per strawberry croissant is $6. Therefore, the objective function can be represented as:

Maximize: $4x_1 + 6x_2$

The constraints are:
1. The store expects to sell at most 1200 croissants: $x_1 + x_2 \leq 1200$
2. The store wants to spend at most $6000, with $3 for each chocolate croissant and $5 for each strawberry croissant: $3x_1 + 5x_2 \leq 6000$
3. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'chocolate croissants'), ('x2', 'strawberry croissants')],
    'objective_function': '4*x1 + 6*x2',
    'constraints': [
        'x1 + x2 <= 1200',
        '3*x1 + 5*x2 <= 6000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

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

# Define the objective function
model.setObjective(4*x1 + 6*x2, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(x1 + x2 <= 1200, name="total_croissants")
model.addConstr(3*x1 + 5*x2 <= 6000, name="total_cost")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution found: chocolate croissants = {x1.varValue}, strawberry croissants = {x2.varValue}")
else:
    print("No optimal solution found")
```