## Symbolic Representation

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

Let's define the variables:
- $x_1$ = Number of regular donuts
- $x_2$ = Number of jelly-filled donuts

The objective is to maximize the total profit. The profit per regular donut is $2, and the profit per jelly-filled donut is $3.

## Objective Function
The objective function to maximize is: $2x_1 + 3x_2$

## Constraints
1. The store pays $4 for each regular donut and $6 for each jelly-filled donut, and the total cost should not exceed $5000: $4x_1 + 6x_2 \leq 5000$
2. The store expects to sell at most 1000 donuts: $x_1 + x_2 \leq 1000$
3. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

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

## Gurobi Code in Python
```python
import gurobipy as gp

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

# Define variables
x1 = model.addVar(name="regular_donuts", lb=0, ub=gp.GRB.INFINITY, vtype=gp.GRB.CONTINUOUS)
x2 = model.addVar(name="jelly_filled_donuts", lb=0, ub=gp.GRB.INFINITY, vtype=gp.GRB.CONTINUOUS)

# Objective function: Maximize 2*x1 + 3*x2
model.setObjective(2*x1 + 3*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(4*x1 + 6*x2 <= 5000, name="Cost_Constraint")
model.addConstr(x1 + x2 <= 1000, name="Donut_Sales_Constraint")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"Number of regular donuts: {x1.varValue}")
    print(f"Number of jelly-filled donuts: {x2.varValue}")
    print(f"Maximum Profit: {model.objVal}")
else:
    print("The model is infeasible or unbounded.")
```