To solve the optimization problem described, we first need to convert the natural language description into a symbolic representation. This involves defining variables for the quantities of interest (number of entry-level devices and premium devices sold), formulating the objective function that represents the total profit, and listing the constraints based on demand and total sales capacity.

Let's define:
- $x_1$ as the number of entry-level devices sold,
- $x_2$ as the number of premium devices sold.

The objective is to maximize profit. Given that the company makes a $300 profit for each entry-level device and a $200 profit for each premium device, the objective function can be represented algebraically as:
\[ \text{Maximize: } 300x_1 + 200x_2 \]

The constraints are:
1. The daily demand for entry-level devices is at most 20: \( x_1 \leq 20 \)
2. The daily demand for premium devices is at most 15: \( x_2 \leq 15 \)
3. The company can only sell at most 30 devices total per day: \( x_1 + x_2 \leq 30 \)
4. Non-negativity constraints, since the number of devices sold cannot be negative: \( x_1 \geq 0, x_2 \geq 0 \)

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'number of entry-level devices sold'), ('x2', 'number of premium devices sold')],
    'objective_function': '300*x1 + 200*x2',
    'constraints': ['x1 <= 20', 'x2 <= 15', 'x1 + x2 <= 30', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's write the Gurobi code in Python to solve this linear programming problem:
```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(lb=0, ub=20, vtype=GRB.INTEGER, name="entry_level_devices")
x2 = m.addVar(lb=0, ub=15, vtype=GRB.INTEGER, name="premium_devices")

# Set the objective function
m.setObjective(300*x1 + 200*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 30, "total_devices")
m.addConstr(x1 <= 20, "entry_level_demand")
m.addConstr(x2 <= 15, "premium_demand")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of entry-level devices to sell: {x1.x}")
    print(f"Number of premium devices to sell: {x2.x}")
    print(f"Total profit: ${300*x1.x + 200*x2.x}")
else:
    print("No optimal solution found")
```