To solve this optimization problem, we first need to define the decision variables and the objective function. Let's denote:

- \(x_A\) as the number of packages of Alnolyte produced daily.
- \(x_B\) as the number of packages of Blenzoate produced daily.

The objective is to maximize revenue. Given that each package of Alnolyte sells for $7 and each package of Blenzoate sells for $10, the total revenue (\(R\)) can be represented by the equation:
\[ R = 7x_A + 10x_B \]

Now, let's consider the constraints:

1. **Automatic Device Time Constraint**: The automatic device is available for at most 500 minutes per day. Given that Alnolyte production requires 5 minutes of automatic device time and Blenzoate production requires 7 minutes, we have:
\[ 5x_A + 7x_B \leq 500 \]

2. **Human-Operated Device Time Constraint**: Similarly, the human-operated device is available for at most 500 minutes per day, with Alnolyte requiring 4 minutes and Blenzoate requiring 3 minutes:
\[ 4x_A + 3x_B \leq 500 \]

3. **Non-Negativity Constraints**: Since we cannot produce a negative number of packages, both \(x_A\) and \(x_B\) must be non-negative:
\[ x_A \geq 0 \]
\[ x_B \geq 0 \]

Given these constraints and the objective function, we can now formulate this problem in Gurobi using Python.

```python
from gurobipy import *

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

# Define variables
x_A = m.addVar(vtype=GRB.CONTINUOUS, name="Alnolyte", lb=0)
x_B = m.addVar(vtype=GRB.CONTINUOUS, name="Blenzoate", lb=0)

# Set the objective function
m.setObjective(7*x_A + 10*x_B, GRB.MAXIMIZE)

# Add constraints
m.addConstr(5*x_A + 7*x_B <= 500, "Automatic_Device_Time")
m.addConstr(4*x_A + 3*x_B <= 500, "Human_Operated_Device_Time")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Alnolyte production: {x_A.x}")
    print(f"Blenzoate production: {x_B.x}")
    print(f"Maximum revenue: ${7*x_A.x + 10*x_B.x:.2f}")
else:
    print("No optimal solution found.")
```