To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's denote the number of regular donuts as \(x_1\) and the number of jelly-filled donuts as \(x_2\).

The objective is to maximize the total monthly profit. The profit per regular donut is $2, and per jelly-filled donut is $3. Therefore, the objective function can be written as:
\[ \text{Maximize:} \quad 2x_1 + 3x_2 \]

The constraints are:
1. The store expects to sell at most 1000 donuts:
\[ x_1 + x_2 \leq 1000 \]
2. The store wants to spend at most $5000 in buying donuts from the bakery, with each regular donut costing $4 and each jelly-filled donut costing $6:
\[ 4x_1 + 6x_2 \leq 5000 \]
3. Non-negativity constraints since the number of donuts cannot be negative:
\[ x_1 \geq 0, \quad x_2 \geq 0 \]

Symbolic representation in JSON format:
```json
{
    'sym_variables': [('x1', 'number of regular donuts'), ('x2', 'number of jelly-filled donuts')],
    'objective_function': '2*x1 + 3*x2',
    'constraints': ['x1 + x2 <= 1000', '4*x1 + 6*x2 <= 5000', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's implement this problem using Gurobi in Python:
```python
from gurobipy import *

# Create a new model
m = Model("donut_optimization")

# Add variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="regular_donuts")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="jelly_filled_donuts")

# Set the objective function
m.setObjective(2*x1 + 3*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 1000, "total_donuts")
m.addConstr(4*x1 + 6*x2 <= 5000, "budget")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Regular donuts: {x1.x}")
    print(f"Jelly-filled donuts: {x2.x}")
    print(f"Total profit: {m.objVal}")
else:
    print("No optimal solution found")
```