To solve the given problem, we first need to convert the natural language description into a symbolic representation. Let's denote the number of burgers as $x_1$ and the number of orders of fries as $x_2$. 

The objective function is to minimize the total cost, which can be represented as $7x_1 + 3x_2$, since each burger costs $7 and each order of fries costs $3.

The constraints are:
- The total calories from burgers and fries should be at least 3000: $500x_1 + 300x_2 \geq 3000$
- The total protein from burgers and fries should be at least 150 grams: $30x_1 + 5x_2 \geq 150$
- The number of burgers and orders of fries cannot be negative: $x_1 \geq 0, x_2 \geq 0$

Now, let's represent this problem in the required JSON format:

```json
{
    'sym_variables': [('x1', 'number of burgers'), ('x2', 'number of orders of fries')],
    'objective_function': '7*x1 + 3*x2',
    'constraints': ['500*x1 + 300*x2 >= 3000', '30*x1 + 5*x2 >= 150', 'x1 >= 0', 'x2 >= 0']
}
```

Next, we'll write the Gurobi code in Python to solve this linear programming problem:

```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(7*x1 + 3*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(500*x1 + 300*x2 >= 3000, "calories")
m.addConstr(30*x1 + 5*x2 >= 150, "protein")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of burgers: {x1.x}")
    print(f"Number of orders of fries: {x2.x}")
    print(f"Total cost: {7*x1.x + 3*x2.x}")
else:
    print("No optimal solution found")
```