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

- $x_1$ as the number of packages of Alnolyte produced daily.
- $x_2$ as the number of packages of Blenzoate produced daily.

The objective function aims to maximize revenue. Given that each package of Alnolyte sells for $7 and each package of Blenzoate sells for $10, the objective function can be represented algebraically as:

\[ \text{Maximize:} \quad 7x_1 + 10x_2 \]

The constraints are based on the availability of the processing devices. Each device is available for at most 500 minutes per day. The time requirements for producing each type of compound are as follows:

- Alnolyte: 5 minutes on the automatic device and 4 minutes on the human-operated device.
- Blenzoate: 7 minutes on the automatic device and 3 minutes on the human-operated device.

Thus, the constraints can be represented as:

\[ 5x_1 + 7x_2 \leq 500 \] (Automatic device constraint)
\[ 4x_1 + 3x_2 \leq 500 \] (Human-operated device constraint)

Additionally, $x_1$ and $x_2$ must be non-negative since they represent the number of packages produced:

\[ x_1 \geq 0 \]
\[ x_2 \geq 0 \]

Given this symbolic representation, we can now encode this problem into a JSON format for clarity:

```json
{
    'sym_variables': [('x1', 'Alnolyte'), ('x2', 'Blenzoate')], 
    'objective_function': '7*x1 + 10*x2', 
    'constraints': ['5*x1 + 7*x2 <= 500', '4*x1 + 3*x2 <= 500', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's translate this into Gurobi code in Python:

```python
from gurobipy import *

# Create a model
m = Model("Chemical_Plant_Optimization")

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

# Set the objective function
m.setObjective(7*x1 + 10*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(5*x1 + 7*x2 <= 500, name="Automatic_Device_Constraint")
m.addConstr(4*x1 + 3*x2 <= 500, name="Human_Operated_Device_Constraint")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Alnolyte: {x1.x}")
    print(f"Blenzoate: {x2.x}")
    print(f"Maximum Revenue: {m.objVal}")
else:
    print("No optimal solution found")
```