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

- $x_1$ as the number of regular refrigerators made each day.
- $x_2$ as the number of energy-efficient refrigerators made each day.

The objective function is to maximize profit. Given that the profit per regular refrigerator sold is $50 and the profit per energy-efficient refrigerator sold is $80, the objective function can be represented algebraically as:

\[ \text{Maximize:} \quad 50x_1 + 80x_2 \]

The constraints based on the problem description are:

1. Demand for regular refrigerators: $x_1 \geq 25$
2. Demand for energy-efficient refrigerators: $x_2 \geq 40$
3. Production limit for regular refrigerators: $x_1 \leq 100$
4. Production limit for energy-efficient refrigerators: $x_2 \leq 70$
5. Minimum total production to satisfy the contract: $x_1 + x_2 \geq 90$

In symbolic notation, we have:

```json
{
    'sym_variables': [('x1', 'number of regular refrigerators'), ('x2', 'number of energy-efficient refrigerators')], 
    'objective_function': '50*x1 + 80*x2', 
    'constraints': ['x1 >= 25', 'x2 >= 40', 'x1 <= 100', 'x2 <= 70', 'x1 + x2 >= 90']
}
```

Now, let's implement this optimization problem using Gurobi in Python:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.INTEGER, name="regular_refrigerators")
x2 = m.addVar(lb=0, vtype=GRB.INTEGER, name="energy_efficient_refrigerators")

# Set the objective function
m.setObjective(50*x1 + 80*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 >= 25, "regular_demand")
m.addConstr(x2 >= 40, "efficient_demand")
m.addConstr(x1 <= 100, "regular_limit")
m.addConstr(x2 <= 70, "efficient_limit")
m.addConstr(x1 + x2 >= 90, "total_contract")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Regular Refrigerators: {x1.x}")
    print(f"Energy Efficient Refrigerators: {x2.x}")
    print(f"Total Profit: ${50*x1.x + 80*x2.x}")
else:
    print("No optimal solution found")
```