To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints algebraically.

Let's define:
- \(x_1\) as the amount invested in the vaccine industry,
- \(x_2\) as the amount invested in the meat-replacement industry.

The objective is to maximize the return on investment, which can be represented by the objective function:
\[ \text{Maximize:} \quad 0.05x_1 + 0.07x_2 \]

Given the constraints:
1. The total investment is $100,000:
\[ x_1 + x_2 = 100,000 \]
2. At least 60% of the investment should be in the meat-replacement industry:
\[ x_2 \geq 0.6 \times 100,000 \]
\[ x_2 \geq 60,000 \]
3. At most 30% of the investment can be in the vaccine industry:
\[ x_1 \leq 0.3 \times 100,000 \]
\[ x_1 \leq 30,000 \]

Also, \(x_1\) and \(x_2\) should be non-negative since they represent amounts of money.

Thus, the symbolic representation in JSON format is:
```json
{
    'sym_variables': [('x1', 'amount invested in the vaccine industry'), ('x2', 'amount invested in the meat-replacement industry')],
    'objective_function': '0.05*x1 + 0.07*x2',
    'constraints': ['x1 + x2 = 100000', 'x2 >= 60000', 'x1 <= 30000', 'x1 >= 0', 'x2 >= 0']
}
```

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

```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(name="vaccine_investment", lb=0)
x2 = m.addVar(name="meat_replacement_investment", lb=0)

# Set the objective function
m.setObjective(0.05*x1 + 0.07*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 == 100000, name="total_investment")
m.addConstr(x2 >= 60000, name="meat_replacement_minimum")
m.addConstr(x1 <= 30000, name="vaccine_maximum")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Amount invested in vaccine industry: {x1.x}")
    print(f"Amount invested in meat-replacement industry: {x2.x}")
else:
    print("No optimal solution found.")

```